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12 Data Scientist Resume Examples - Here's What Works In 2024

Data scientists are one of the hottest jobs of 2023. however, it’s also one of the most analytical, results-driven, and requires superb use of numbers. if you can show that on your resume, you’ll be on your way to a nice career as a data scientist. here are five data scientist resume templates to help you get an idea of what to put in your resume..

If career growth is one of your main qualifications for your next job, a career in data science is perfect for you. According to Towards Data Science , it’s the fastest-growing job on LinkedIn with an estimated over 11 million jobs by 2026. And it deserves to have such a bright future. You can apply for this job in several industries like e-commerce, IT, business, and much more. Because this field is so versatile, you can apply your skills somewhere that would greatly benefit others, not just a company. For example in healthcare, you can help visualize and manage data necessary for operation procedures. For a job like this, you need to be good with numbers and data. The ability to use statistics, analyze complex data, simplify it, and present it more easily for others are all necessary components of the job. You’ll need to display these skills, plus some experience with computer programs like Amazon Web Services to handle big data, in your resume. Today, we’ll be sharing with you the tips you need to make a data scientist resume that recruiters will look at.

Data Scientist Resume Templates

Jump to a template:

• Data Scientist
• Senior Data Scientist
• Entry Level Data Scientist
• Data Science Manager
• Data Science Vice President
• Junior Data Scientist
• Career Change into Data Science

Jump to a resource:

• Keywords for Data Scientist Resumes

Data Scientist Resume Tips

• Action Verbs to Use
• Bullet Points on Data Scientist Resumes
• Frequently Asked Questions
• Related Data & Analytics Resumes

Get advice on each section of your resume:

Template 1 of 12: Data Scientist Resume Example

A data scientist uses and processes raw data to discover interesting insights that help organizations make more informed decisions. They are part of the entire life cycle of data science projects. This means they work on collecting and storing data, as well as in data processing, developing data models, data analysis, and visualization. Cloud migration is now an in-demand skill for data scientists, due to the rapid adaptation of cloud services. Hence, it might be a good idea to include cloud migration skills on your resume.

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Tips to help you write your Data Scientist resume in 2024

   include up-to-date data analysis or big data skill sets on your resume, like tinyml..

Data science is a fast-changing field, and hiring managers particularly at tech companies or startups love when candidates include recent technologies. One example is TinyML or other ML algorithms. Machine learning algorithms are perfect for processing large sets of data, especially when working with cloud-based systems with unlimited bandwidth. It might be worth including a project on your resume where you used ML or insights from an ML algorithm to improve the bottom line at your company (if you drove revenue or saved costs as a result of running a data science algorithm, hiring managers will be thrilled).

   Indicate your proficiency in data visualization tools like Tableau or Google Charts.

Mention projects in which you used your data visualization skills to present your insights. Data visualization plays a huge role in data science projects, so it’s important to demonstrate you have experience in this area.

Skills you can include on your Data Scientist resume

Template 2 of 12: data scientist resume example.

Because you are working with data that provide to you or you provide other departments data to use, you need to display successful collaboration with results in your resume. This sample does this by talking about what company goals were accomplished with other teams using metrics to highlight the achievements.

   Numbers and metrics relevant to data scientists

You can see examples of metrics to go with the companies’ achievements. For example, this person increased “customer traffic by 75%”, and generated “$1 million in wealth management sales”. Data science is always aligned with company KPIs, so list your achievements in a way that describes how you solved a company’s problem.

   Strong action verbs related to data scientists

When you read this sample, you’ll see words like “implemented”, “optimize”, and “reduced.” All these are action verbs that communicate the ability to do/succeed in a task. Include strong action verbs in your resume that communicates your ability to organize projects and collaborate with others.

Template 3 of 12: Senior Data Scientist Resume Example

Senior data scientists outline project requirements, delegate tasks to junior data scientists, monitor their performance and carry out upper-level responsibilities. Their purpose is to drive companies to success by using data analytics. Your potential employer might expect you to have extensive experience in data science, so it’s important to demonstrate seniority on your resume. You should prioritize relevant job experience and highlight your leadership background.

Tips to help you write your Senior Data Scientist resume in 2024

   indicate your proficiency in r, python, or other relevant programming languages by mentioning previous projects in which you used them..

Since most companies are generating a large amount of data, you need specific programming languages such as R or Python to process them. That’s why your potential employer might be looking for an experienced senior data scientist in these programming languages.

   Demonstrate experience in formulating and overseeing data-centered projects.

A senior data scientist is a leadership role. You will be supervising other junior data scientists to ensure they follow certain standards and processes, whether that involves cleaning or exploration. That’s why it is important to demonstrate on your resume that you have experience with developing and monitoring these types of projects.

Skills you can include on your Senior Data Scientist resume

Template 4 of 12: senior data scientist resume example.

If you’re trying to climb up to the top of the data scientist ladder, you need to show that you excelled in lower positions. Don’t forget to list what you did that earned you an upper-level role in your previous job. Recruiters love to see that you desire to grow. Talking about your transitions is key in this kind of resume.

   Shows growth in promotions

In the sample, you see that there was a promotion within a short amount of time at a company. If you had a promotion, emphasize it by separating the job titles and explaining what work you’ve done that contributed to you getting promoted.

   Numbers and metrics relevant to senior data scientists

Don’t just list promotional achievements without also providing the metrics. Recruiters want to see how you’ve been beneficial to the previous company, and numbers are a great way to show your achievements. That gives recruiters an idea of how you can help their company out.

Template 5 of 12: Entry Level Data Scientist Resume Example

As an entry level data scientist, you'll be dipping your toes into the world of analyzing and interpreting complex data sets to help businesses make informed decisions. While the demand for data scientists has been booming in recent years, competition for entry-level roles can be fierce. To stand out, your resume should showcase your technical skills and demonstrate your ability to turn raw data into valuable insights for the company. Think about highlighting projects where you've used relevant programming languages, machine learning techniques, and data visualization tools. In addition to showcasing your technical expertise, don't forget to highlight any internships or relevant work experience you have related to data analysis. Companies are not just looking for technical wizards; they are also seeking individuals who can work well with others, translate complex findings into understandable insights, and ultimately drive business growth. Make sure to include any instances where you've collaborated with cross-functional teams or presented data-driven findings to non-technical stakeholders.

Tips to help you write your Entry Level Data Scientist resume in 2024

   show off your technical skills.

As an entry level data scientist, you should emphasize your programming abilities and proficiency in languages like Python, R, and SQL. Additionally, mention any experience working with data analysis tools, such as Tableau, to demonstrate your ability to visualize and communicate results effectively.

   Highlight your problem-solving capabilities

Data scientists need to be adept at solving complex problems and uncovering insights from raw data. Use your resume to share examples of how you've approached and solved data-related challenges, emphasizing your analytical mindset, creativity, and critical thinking skills.

Skills you can include on your Entry Level Data Scientist resume

Template 6 of 12: entry level data scientist resume example.

Right out of college, you may not have much experience in the field. To supplement that, use your experience in clubs and activities, class projects, and useful coursework to help highlight your knowledge on the subject. Internship experience is essential, as well; any numeric results or accomplishments should be acknowledged. This sample does so by listing the percentages of costs, labor, and hours reduced thanks to their work.

   Strong data scientist technical skills

Not only are key skills listed in the skills section (things like MATLAB or SQL), you can also see this sample mention the use of some of these skills throughout their experience. You should also include skills that are relevant to data science jobs that you have - review the job description that you're applying to for skills the job is looking for.

   University projects relevant to data scientists

Class projects are good examples of how a recent grad has applied critical job skills. In the descriptions, it also lists awards won. This shows that the projects they worked on were successful in applying what they learned to get results.

Template 7 of 12: Data Science Manager Resume Example

A data science manager has an administrative and technical role. They are responsible for guiding and overseeing the data science team. Hence, they will determine project outlines, deadlines, and priorities, and ensure team members follow specifications. As a data science manager, you should ideally have a master’s degree in data science or equivalent experience. You can take your resume to another level by demonstrating your impact on previous projects’ results. This way, you are showcasing your tangible value.

Tips to help you write your Data Science Manager resume in 2024

   include your data science certifications on your resume..

Your data science manager resume should highlight your academic value and expertise, and certification is a great way to demonstrate that. These are third-party validated credentials that exhibit your skills and years of experience.

   Highlight your project management skills through relevant work experience.

Data science managers should have project management skills to successfully drive success to the data science team. Recruiters are looking for past evidence of assigning tasks, prioritizing deliverables, providing feedback, conducting research, and ensuring team members’ performance. To highlight this, include action verbs like "Led" or "Managed".

Skills you can include on your Data Science Manager resume

Template 8 of 12: data science manager resume example.

To be a successful manager in any role, you need to have the experience of a manager. A focus on team management and leading a team to great results are examples you should list on your resume. Showing recruiters that you can lead a team or data science project that brings high-yield results is what will set your resume apart from other applicants. Data science is all about using data to drive decision-making and top-level KPIs, so make sure you add accomplishments to your resume that highlight how your work has affected your company’s bottom line.

   Emphasis on managerial skills

You can see in the experience section of this sample how they led a few projects. They discuss what was done, who they worked with, and how big a team they had. Follow a similar layout in your resume so recruiters can see that you can lead data science teams.

   Tailored to the data science industry

One way that you can get your resume past the filtering system, or ATS, is to use specific keywords that are found throughout the job description. In this sample, you see keywords like “training and peer-mentoring”, “data systems”, and “regression analysis.”

Template 9 of 12: Data Science Vice President Resume Example

A Data Science Vice President sits at the intersection of data analytics, business strategy, and leadership. In recent years, your role has evolved from pure data analysis to one where you're expected to guide an entire organization's data strategy. As companies increasingly rely on data-driven decision-making, you're not just crunching numbers but explaining their implications to non-technical executives. When crafting a resume for this role, remember companies are looking for a strategic thinker who can leverage data to drive business growth, not just a seasoned analyst. As the field becomes more competitive, hiring managers are expecting more than just top-notch technical skills. They want to see a track record of transforming raw data into actionable insights that drive business results. They're also looking for leaders who can build and guide high-performing data science teams. So, make sure your resume reflects these demands and trends.

Tips to help you write your Data Science Vice President resume in 2024

   highlight strategic leadership.

As a Data Science Vice President, you're expected to be a strategic leader. Highlight instances where you've used data to inform business strategy. Show how you've influenced decision-making at the executive level by translating complex data into digestible insights.

   Focus on Team Building and Management

This role isn't just about your expertise with data, but also your ability to lead a team. Detail your experience in building, leading, and mentoring data science teams. If you've overseen sizeable teams or managed across different locations, ensure that it shines on your resume.

Skills you can include on your Data Science Vice President resume

Template 10 of 12: data science vice president resume example.

Like any VP role, the position of vice president of data science needs strong managerial skills. Not only will you need to manage a team, but that team will also have to consist of managers. Your goal is to implement and execute company-wide goals that greatly benefit the company. This sample lists out the processes done while managing managers lower on the corporate ladder, to bring in an increase of profit or a decrease in costs (or increase in productivity).

In this sample, the positions listed are all higher than the ones listed below. That shows recruiters that you have the ambition to climb to the top. Additionally, with each upper management role, you see growth in the people they work with; they started with “hired 8 new candidates” and are now “worked closely with a cross-functional team.” Show your incline in managerial responsibilities in your resume.

   Focused on the vice president of data science role

In the upper management positions of this sample, you see how it talks about working with other department teams to deliver results that are often well over 40%. Positive metrics like this help show your abilities as a capable vice president.

Template 11 of 12: Junior Data Scientist Resume Example

Junior data scientists are just data scientists that have under five years of industry experience, or have recently made a career change into the field. The title is sometimes used interchangeably with the regular 'data scientist', so you can use this template whether or not you're a junior data scientist or have some experience in the field.

Tips to help you write your Junior Data Scientist resume in 2024

   numbers and metrics relevant to data scientists, and good use of skills relevant to data scientists..

You can see examples of metrics to go with the companies’ achievements. Plus, all the skills mentioned are very relevant to the data science and engineering field.

   Good use of space

The two-column in this data scientist resume template prioritizes the work experience sections, while maximizing the content into the resume. The resume does not look overcrowded and uses reasonable margins. Not all two column templates are ATS-compatible, but this one is when it is saved as PDF and passed through a resume screener.

Skills you can include on your Junior Data Scientist resume

Template 12 of 12: career change into data science resume example.

If you're trying to break into data science, but don't have formal data science experience yet, use a template like this one.

Tips to help you write your Career Change into Data Science resume in 2024

   stress transferrable skills from your previous experiences.

Even if you didn't do data science work in your previous professional roles, you have technical experience as well as leadership, teamwork and analytical skill sets.

   Use keywords and skills from the new industry on your career change resume

To get past the applicant tracking systems and resume screeners, it's important that you use the right keywords for your target job, which in this case is a data science position. Even though you might have sales or product marketing experience, use keywords that are specific to data science only - including things like SQL/database experience, ML/AI experience, and other data preparation tools and techniques.

Skills you can include on your Career Change into Data Science resume

We reached out to hiring managers and recruiters at top companies like Google, Amazon, and Microsoft to gather their best tips for creating a standout data scientist resume. Here's what they shared:

   Highlight your technical skills

Make sure to showcase your proficiency in the key technical skills required for data science roles, such as:

• Programming languages (Python, R, SQL)
• Machine learning frameworks (TensorFlow, PyTorch, scikit-learn)
• Data visualization tools (Tableau, PowerBI, Plotly)
• Big data technologies (Hadoop, Spark, Hive)

Don't just list the skills, but provide specific examples of how you've used them in projects or previous roles. Quantify your impact whenever possible, like 'Built machine learning models using Python and scikit-learn to improve customer churn prediction accuracy by 25%.'

   Showcase your projects and their impact

Hiring managers want to see evidence of your ability to apply data science techniques to real-world problems. Include 2-3 of your most impressive projects, highlighting:

• The business problem or question you were trying to solve
• The datasets and techniques you used (e.g., data cleaning, feature engineering, model selection)
• The results and impact of your work, quantified if possible (e.g., increased revenue, reduced costs, improved efficiency)

Even if the projects were part of coursework or personal learning, they can still effectively demonstrate your skills and problem-solving approach.

   Tailor your resume to the job description

Data science roles can vary significantly between companies and industries. Carefully review the job description for each position you apply to, and customize your resume accordingly.

Look for key skills, tools, and domain knowledge mentioned in the job requirements, and make sure to emphasize your relevant experience in those areas. For example, if the job heavily focuses on natural language processing (NLP), highlight any NLP projects or coursework you've completed.

   Provide context for your achievements

When describing your accomplishments, provide enough context to help the hiring manager understand the significance of your work. Instead of simply stating what you did, explain why it mattered to your team or organization.

• Developed a machine learning model to predict customer churn
• Developed a machine learning model to predict customer churn, enabling proactive retention efforts that reduced churn by 20% and saved the company $500K annually

By connecting your work to business outcomes, you demonstrate your ability to drive meaningful impact and think strategically.

   Show your communication and collaboration skills

Data scientists rarely work in isolation; they need to effectively communicate insights to stakeholders and collaborate with cross-functional teams. Highlight experiences that showcase these critical soft skills:

• Presenting findings to executive leadership
• Collaborating with engineers to deploy models in production
• Partnering with domain experts to define business problems and requirements

Worked closely with product and marketing teams to develop customer segmentation models, leading to personalized marketing campaigns that increased conversion rates by 30%.

By emphasizing your communication and collaboration abilities, you show that you can bridge the gap between technical and non-technical audiences.

   Demonstrate continuous learning and growth

The field of data science is constantly evolving, with new techniques and tools emerging regularly. Hiring managers want candidates who are committed to ongoing learning and staying up-to-date with industry trends.

Highlight any relevant coursework, certifications, or independent learning you've undertaken to expand your data science skills. This could include:

• Online courses (e.g., Coursera, edX, Udacity)
• Participation in data science competitions (e.g., Kaggle)
• Attendance at conferences or workshops
• Contributions to open-source projects

By showcasing your continuous learning efforts, you demonstrate your passion for the field and your ability to adapt to new challenges and technologies.

Data science is a broad job category. You could have a focus on designing machine learning algorithms/predictive analytics, or data visualization, or mathematics and statistics. You may even have more of a focus on the business side of things. No matter which area of data science you’re in, follow these tips to help you tailor the perfect resume.

   Think it all through first

Before you start filling out your resume, have a brainstorming session. What programs, teamwork-based, or other hard skills do you have that are relevant? What are some of the achievements you’ve had on the job? Did you do (and succeed) any data science projects? Have an idea of all of that first. Then, write it out in your experience. The key is to ensure you’re including quite a few metrics. A role that involves a lot of data requires someone who is good at handling big numbers and knows how to effectively use the info. If that data involves cooperation from another department, include that as well.

   Edit it so the resume is fitting for the job description

When you finish writing it, reread the job description. How well do you think you did in matching your resume’s keywords with the job opening’s keywords? Have you left out the filler information? (You should; only make space for what’s necessary, especially when you have lots of experience.)

  Include personal projects

For those of you who are transitioning from a different --but possibly somewhat relevant-- field, or are fresh out of school, projects are your friend. Just be certain to briefly describe what the project was for, what you accomplished, and provide metrics. Let’s say that you want to enter the finance field; an example project you can complete is a credit card fraud detector. You’ll use Python to track transaction history and spending habits, and use regression analysis to accurately track the two. You can also include links to your Github profile too, especially if you have a project that’s particularly relevant.

   Talk about collaborations with teams

For those of you who are veterans in the field, focus on your work done with other departments. Data science is all about working with other teams to drive business decisions, and teamwork is a skill that recruiters look for. What collaborative projects have you done that exemplifies this? Are/were you in charge of leading a team that brought in lots of revenue or extra work time? Have you been in charge of a major development project? Detail this information in your experience.

Writing Your Data Scientist Resume: Section By Section

  header, 1. put your name front and center.

Your name should be the most prominent element in your header, typically styled in a larger font than the rest of your contact details. This makes it easy for hiring managers to remember who you are.

Here's an example of how to format your name:

Avoid nicknames or unprofessional email handles:

• Johnny 'The Data Wizard' Smith
• [email protected]

2. Include essential contact details

Under your name, provide your key contact information:

• Phone number
• Professional email address
• Location (City, State)
• LinkedIn URL

Example of how to format this:

[email protected] | 555-123-4567 | Seattle, WA | linkedin.com/in/johnsmith

Avoid providing unnecessary personal details like your full mailing address or multiple phone numbers, which can clutter your header.

3. Optionally include your top data science credential

If you have an impressive, industry-recognized data science certification or credential, consider featuring it after your name to immediately boost your credibility. For example:

John Smith, CFA [email protected] | 555-123-4567 | Seattle, WA | linkedin.com/in/johnsmith

However, avoid listing multiple credentials or irrelevant certifications that may distract from your core qualifications as a data scientist.

  Summary

A resume summary is an optional section that sits at the top of your resume, just below your name and contact information. While not required, it can be a valuable addition for data scientists, particularly those with extensive experience or looking to transition into the field. A well-crafted summary provides context and highlights your most relevant qualifications, setting the stage for the rest of your resume.

When writing your summary, focus on your key strengths, experience, and accomplishments that align with the data scientist role you're targeting. Avoid using an objective statement, as it tends to focus on your goals rather than what you can bring to the employer. Instead, think of your summary as a snapshot of your professional profile, showcasing why you're the ideal candidate for the position.

To learn how to write an effective resume summary for your Data Scientist resume, or figure out if you need one, please read Data Scientist Resume Summary Examples , or Data Scientist Resume Objective Examples .

1. Highlight your technical expertise

As a data scientist, your technical skills are crucial to your success in the role. Use your summary to showcase your proficiency in key areas such as:

• Programming languages (e.g., Python, R, SQL)
• Machine learning algorithms and frameworks
• Data visualization tools (e.g., Tableau, PowerBI)
• Big data technologies (e.g., Hadoop, Spark)

For example:

Data Scientist with 5+ years of experience leveraging Python, R, and SQL to build and deploy machine learning models. Proficient in data visualization using Tableau and PowerBI, with expertise in big data technologies like Hadoop and Spark.

2. Quantify your impact

Hiring managers love to see concrete examples of how you've driven results in your previous roles. Use metrics and data to quantify your impact, demonstrating the value you've brought to your past employers. For example:

• Experienced data scientist with a passion for solving complex problems
• Collaborated with cross-functional teams to develop and implement data-driven solutions

While these statements provide some insight into your experience, they don't give the hiring manager a clear sense of your impact. Instead, try something like:

• Developed machine learning models that increased customer retention by 15% and reduced churn by 20%
• Led a team of 5 data scientists to optimize supply chain processes, resulting in $2M in annual cost savings

3. Showcase your industry knowledge

Demonstrating your understanding of the industry you're targeting can help you stand out from other applicants. Use your summary to highlight your experience working with industry-specific datasets, tools, or challenges. For example:

Data Scientist with 7+ years of experience in the financial services industry. Expertise in developing predictive models for fraud detection, risk assessment, and customer segmentation. Proficient in using industry-specific tools like Bloomberg Terminal and FactSet.

By showcasing your industry knowledge, you demonstrate to the hiring manager that you understand the unique challenges and opportunities within their sector, making you a more compelling candidate.

  Experience

Your work experience section is a key part of your data scientist resume. After all, it's where you show that you have the skills and experience to excel in the role.

Here are some tips to make sure your work experience section is as strong as it can be:

1. Highlight your technical skills

As a data scientist, you likely have experience with a variety of programming languages, tools, and frameworks. Make sure to highlight the ones that are most relevant to the job you're applying for.

Here are some examples of how you might showcase your technical skills:

• Developed machine learning models using Python, scikit-learn, and TensorFlow to predict customer churn with 95% accuracy
• Analyzed large datasets using SQL and Tableau to identify opportunities for cost savings and process improvements
• Built and maintained data pipelines using Apache Spark and Hadoop to process and analyze terabytes of data

Not sure if your resume highlights your technical skills effectively? Try using Targeted Resume to see how well your resume matches up with the job description. It can help you identify any key skills or keywords you may be missing.

Whenever possible, use numbers and metrics to quantify the impact of your work. This helps hiring managers understand the value you brought to your previous roles.

Here are some examples of how you might quantify your impact:

• Increased revenue by 20% by developing a predictive model to identify high-value customers
• Reduced data processing time by 50% by implementing a new data pipeline architecture
• Improved model accuracy by 10% by feature engineering and hyperparameter tuning

Contrast this with examples that don't quantify impact:

• Developed predictive models to identify high-value customers
• Implemented a new data pipeline architecture
• Improved model accuracy through feature engineering and hyperparameter tuning

If you don't have access to specific metrics, you can still quantify your impact by using numbers. For example, you might say "Analyzed data from over 10,000 customers to identify trends and patterns."

3. Showcase your problem-solving skills

Data scientists are often tasked with solving complex problems using data. Use your work experience section to showcase examples of how you've used your problem-solving skills to make an impact.

Here are some examples:

• Identified and resolved data quality issues that were causing inaccurate reporting, resulting in a 15% increase in data accuracy
• Developed a machine learning model to predict equipment failures, reducing downtime by 20% and saving the company $500k annually
• Collaborated with cross-functional teams to identify opportunities for process improvements, resulting in a 25% reduction in cycle time

When describing your problem-solving skills, try to focus on the impact of your work. How did your solutions benefit the company or your team?

4. Highlight your leadership and collaboration skills

While technical skills are important for data scientists, leadership and collaboration skills are also highly valued. Use your work experience section to showcase examples of how you've led projects or collaborated with others.

• Led a team of 5 data scientists to develop a new customer segmentation model, resulting in a 15% increase in marketing campaign effectiveness
• Collaborated with cross-functional teams including marketing, product, and engineering to develop and launch a new product feature that increased user engagement by 20%
• Mentored junior data scientists on best practices for data analysis and modeling, resulting in a 25% improvement in team productivity

If you're applying for a senior-level data scientist role, highlighting your leadership and collaboration skills can help you stand out from other applicants. Consider using Score My Resume to get feedback on how well your resume showcases these skills.

  Education

Your education section shows hiring managers that you have the necessary training and knowledge for the data scientist role. It also helps them gauge your career level. Here are some tips to write an effective education section on your data scientist resume.

1. Put your education at the top if you're a recent grad

If you graduated within the last 1-3 years, place your education section above your work experience. This is because your degree is likely your strongest qualification for the job at this stage in your career.

Include the following details for each degree:

• Name of institution
• Degree earned
• Graduation year
• Relevant coursework, projects, or academic achievements

Education Master of Science in Data Science, ABC University, 2022 Relevant Coursework: Machine Learning, Data Mining, Big Data Analytics, Statistical Modeling Capstone Project: Developed a predictive model for customer churn using Python and TensorFlow

2. Emphasize advanced degrees and certifications

If you have a master's degree, PhD, or professional certifications in data science or a related field, make sure to highlight these in your education section. Advanced credentials demonstrate specialized expertise that can set you apart from other candidates.

Examples of data science certifications to include:

• Certified Analytics Professional (CAP)
• SAS Certified Data Scientist
• IBM Data Science Professional Certificate
• Microsoft Certified: Azure Data Scientist Associate

Education PhD in Computer Science, XYZ University, 2018 Dissertation: A Novel Approach to Sentiment Analysis Using Deep Learning Certifications SAS Certified Data Scientist, 2020 Microsoft Certified: Azure Data Scientist Associate, 2021

3. Keep it brief if you're a senior data scientist

If you have several years of work experience as a data scientist, your education section should be concise. Hiring managers will be more interested in your professional accomplishments than your academic background at this stage.

Here's what a bad example might look like for a senior data scientist:

• Bachelor of Science in Mathematics, DEF University, 2005-2009. Graduated summa cum laude. Relevant coursework: Calculus, Linear Algebra, Probability Theory, Mathematical Statistics. Senior thesis on applications of graph theory.

Instead, keep it short and sweet:

• BS Mathematics, DEF University

Action Verbs For Data Scientist Resumes

The field is all about quantifying aand using data. In your resume, you need to explain what you did with the data you have. In the samples, you’ll see examples of action verbs like “implemented”, “developed”, “coached”, and more. Action verbs like these show that you know how to apply the knowledge you have to your work.

For a full list of effective resume action verbs, visit Resume Action Verbs .

Action Verbs for Data Scientist Resumes

How to write a data scientist resume.

Here are step-by-step instructions on how to write an effective resume for a data scientist role. This guide can be used by both entry-level and experienced data scientists as well as data scientist managers.

Basic steps for writing a Data Scientist resume

1.1: place important information in your header.

Place your name at the top of the resume followed by your professional email address, city/country, and phone number. You could also include the job title of your desired role—e.g., Data Analyst—to tailor your resume to the job. It is a good idea to include links to your professional website and online profiles such as LinkedIn and GitHub.

1.2: Select sections that highlight your most relevant experience

A Data Scientist resume needs sections for experience and education. Unless you are a recent graduate, you should list your experience section first. If you have carried out projects that highlight your data analysis skills, you can include a projects section that briefly describes the projects alongside metrics that show what you accomplished.

Use bullet points to showcase your experience as a Data Scientist

2.1: use the [action verb] + [task] + [metric] format for your bulleted points.

A bulleted list of your achievements in the work experience section will make your resume easy for data science hiring managers to skim. Each bullet point should highlight a specific task or achievement from your previous role. Take a look at the bullet point example below: "Modelled user-engagement framework that reduced churn rate using predictive modeling and clustering that reduced churn rate by 40%." Notice how the bullet point uses an action verb that is relevant to data analysis, "Modelled". We describe a task that was completed and use numbers and metrics to quantify the impact of our achievement.

2.2: Highlight collaborative work and initiative

For mid to senior Data Scientist roles, you will need to demonstrate you can take initiative and work with other departments. Talk about collaborating with other teams to drive business decisions. To land a Data Science Manager role, highlight how you led a team to great results in a data science project.

Get past resume screeners by including the right technical skills

3.1: use word or google docs resume template for your draft, then save it as pdf.

Start your resume with a simple template in Word or Google Docs format. This ensures your resume can be scanned easily by Applicant Tracking Systems, which are software used to screen resumes online. Convert your resume to PDF to ensure the formatting and layout appears correctly to a data science recruiter.

3.2: Use an online resume checker to make sure resume scanners can read your resume

If the ATS cannot read your resume, it will automatically discard your application before a Data Science recruiter gets to see it. Upload your resume for free to a resume scanner to ensure it can be read correctly and that the bullet points and sections are correctly constructed.

3.3: Include a technical skills section

Populate the skills section with hard skills and keywords that the resume filtering software will be looking for. Common skills for Data Scientists include Machine Learning, Python, SQL, R, Data Mining, Statistical Modeling, and Hadoop.

Finalizing your Data Scientist resume

4.1: include resume summary if you are changing careers or are a senior level hire.

While resume objectives are outdated and should never be used, a resume summary is an optional section at the top of your resume that can help direct a recruiter's attention to specific skills and achievements not listed in the rest of the resume. The summary can also include transferable skills for people shifting to Data Science from other careers.

4.2: Reread the job description as you edit your resume

When you finish writing your resume, reread the job description. This will give you a sense of how well your resume matches relevant keywords in the data scientist role. Check whether you have included examples of your impact, such as the amount of savings your company experienced because of the machine learning model that you implemented.

Skills For Data Scientist Resumes

Data science is a number-intensive, data-heavy field. It’s one thing to know how to read the data. You also need to convert that data in a way that makes a company’s overall processes smoother. Your list of skills should aid in showing that. Because you’d be using languages like Python or SQL, it’s important to state it beyond the skills section. Where possible, mention how you used these tools in your experience, whether that’s to process large data sets, discover insights or drive business decisions. If recruiters can see that you know how to use critical tools for the job on your resume, it’ll stand out more. Plus, your resume will get past resume screening tools/ATS since employers often filter resumes out by searching for skills they expect to see. Closely read the job description to find skills to include in your resume.

• Data Science
• Machine Learning
• Artificial Intelligence (AI)
• Deep Learning

Data Mining

• Python (Programming Language)
• Natural Language Processing (NLP)
• Apache Spark
• R (Programming Language)
• Predictive Analytics
• Predictive Modeling
• Software Development
• Statistical Modeling

How To Write Your Skills Section On a Data Scientist Resumes

You can include the above skills in a dedicated Skills section on your resume, or weave them in your experience. Here's how you might create your dedicated skills section:

Skills Word Cloud For Data Scientist Resumes

This word cloud highlights the important keywords that appear on Data Scientist job descriptions and resumes. The bigger the word, the more frequently it appears on job postings, and the more 'important' it is.

How to use these skills?

Resume bullet points from data scientist resumes.

You should use bullet points to describe your achievements in your Data Scientist resume. Here are sample bullet points to help you get started:

Conducted private equity due diligence in $400M portfolio. Performed strategic and analytical valuation of assets based on interviews with experts and created extensive models of the industries; persuaded client to move forward with acquisition

Analyzed data from 25000 monthly active users and used outputs to guide marketing and product strategies; increased average app engagement time by 2x, decrease drop off rate by 30%, and increased shares on social media by 3x over 6 months

Generated insights on customer churn and renewal rates from data tables with 100M rows in SQL

Liaised with marketing to drive email and social media advertising efforts, using predictive modeling and clustering, resulting in a 35% increase in revenue

Reduced signup drop-offs from 65% to 15%, increased user-engagement by 40%, and boosted content generation by 15%, through a combination of user interviews and A/B-testing-driven product flow optimization

For more sample bullet points and details on how to write effective bullet points, see our articles on resume bullet points , how to quantify your resume and resume accomplishments .

Frequently Asked Questions on Data Scientist Resumes

How can i improve my data scientist resume.

• Include a projects section that briefly describes the projects alongside metrics that show what you accomplished. Here, list projects that demonstrate the use of statistical methods, data visualization techniques and predictive models.
• Include the job title for the desired role—Data Scientist—on the resume header below your name. This makes your resume easier for screening software to categorize.
• Include links to your professional website and online profiles such as LinkedIn and GitHub.
• Include a summary section if you are a senior-level hire or are changing careers to direct the recruiter’s attention to transferable skills and exceptional achievements.

How does a data scientist’s resume differ from that of other data analytics roles?

What skills should you put on a data scientist resume, what are strong examples of bullet points i can include in my data scientist work experience.

Modelled a user-engagement framework that reduced churn rate using predictive modelling and clustering that reduced churn rate by 40%. Designed and implemented securities forecasting models, improving stock market forecast accuracy by 15%.

Other Data & Analytics Resumes

Director of Analytics

Solutions Architect

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• Senior Data Scientist Resume Example
• Entry Level Data Scientist Resume Example
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• Data Science Vice President Resume Example
• Junior Data Scientist Resume Example
• Career Change into Data Science Resume Example
• Tips for Data Scientist Resumes
• Skills and Keywords to Add
• Sample Bullet Points from Top Resumes
• All Resume Examples
• Data Scientist CV Examples
• Data Scientist Cover Letter
• Data Scientist Interview Guide
• Explore Alternative and Similar Careers

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The Perfect Data Science Resume (an 8-Step Guide)

If you want a job in data science, you’ll have to get past the “Gatekeeper,” better known as the recruiter or hiring manager. And that means writing a great resume. 

Here’s the hard truth: No matter how many technical skills you have in data science, your career won’t get off the ground without a resume that will get you noticed. It might be unfair that this single document can stand in the way of you and your dream career, but it’s the absolute truth! 

Fortunately, there are some tips and tricks for effective resume writing. These can help you stand out amongst other data science applicants. 

In this post, we’ll reveal some little-known secrets about how to write a great data science resume. You’ll get access to expert testimonials from real hiring managers in the industry as well as some pro-tips for making your resume stand out. Along the way, we’ll be pointing out some crucial mistakes to avoid — the ones recruiters see as red flags!

Need to add some skills to round out your resume? Check out our comprehensive data science course catalog and start learning!

Step #1: Keep Data Science Resumes to One Page

The challenge is to be both thorough and concise. A good resume should only be one page long (even if you have twenty years of relevant experience for the job you’re applying to).

There’s a good reason for this. Hiring managers receive hundreds of resumes. So in the first round, they may take less than 30 seconds to look over each resume and make a decision .

“Let me be honest,” says Stephen Yu, president and chief consultant at data analytics consulting firm Willow Data Strategy . “Before I meet somebody, I spend less than 30 seconds. If that resume doesn’t speak to me, which only happens with one in ten resumes anyway, I’m not even going to call the candidate.”

Resume Red Flag: Lengthy Resumes

The takeaway here? You need to prioritize. Condense your experience to the most-important, most-relevant points so it’s easy to scan.

Lengthy resumes over a page long are no-gos for recruiters! 

Step #2: Customize, Customize, Customize!

Think about it. If you’re a hiring manager, are you more impressed with a generic resume or one that caters to your specific company culture and job requirements? Exactly. 

Yes, it requires more upfront work than the copy-paste approach. But adding small details here and there in accordance with the specific job will most certainly earn you points.

Does that mean you need to do a wholesale rewrite and redesign every time you apply for a job? No. But, at a minimum, look for important keywords and skills mentioned in the job posting and be sure to include those on your resume.

Pro-Tip: Dial In Your Writing Style and Tone

If you’re applying for your dream job, and you’re really looking to impress, you can take things up a notch. Here’s how: Take a look at the company’s website to get an idea of their preferred style and tone. Then, adjust the writing and aesthetics of your resume accordingly.

“You have to find a way to showcase yourself such that when an employer is looking at your resume, they go, ‘This person was sent down from the heavens just for my particular position,’” says SharpestMinds co-founder Edouard Harris.

Step #3: Choose Your Template Wisely

The type of resume template you choose should align with the type of company you’re applying to. 

For example, if you’re applying to companies with a more traditional feel (e.g., the Dells, HPs, and IBMs of the world), try to aim for a more classic, subdued style of resume.

On the other hand, if you’re aiming for a company with more of a startup culture or creative vibe (e.g., Google, Meta, Pinterest, etc.), you can choose a template or create a resume with a little more flair.

The template you choose can also be an organizational tool. A column-style resume can help you fit more information on the page, for example.

Pro-Tip: Use an Online Resume Template

Can you create your own resume from scratch? Sure. But it may be easier to start with creative resume templates from free sites such as Creddle ,  VisualCV , CVMKR , Kittl, or Enhancv . You can also view some great data science resume examples on Beam Jobs.

Step #4: Curate Your Contact Info

Once you choose a resume template, take a second to double-check the contact information section. Your name, headline, and contact information should always be visible at the top of the page.

Why? You don’t want a recruiter or hiring manager to have to search through the whole resume to figure out how to get in touch with you, do you? Make it easy for them! 

Here are some key things to remember about the contact information on your data science resume:

• Simplify your address to just the city and state.
• List a good, working phone number and a professional-looking email address.
• Include your personalized LinkedIn URL.
• Add a GitHub link or personal profile link to your contact information, and make it clickable . 

Make sure your headline (typically found underneath your name) reflects the job you’re looking to get rather than the job you currently have. If your desire is to become a data scientist, your headline should say “Data Scientist” even if you’re currently working as a chef, for example.

Step #5: Include Data Science Projects and Publications

In any good data science resume, the main thing you want to highlight is what you have created. Include a separate section dedicated to your data science projects and publications. Place this information immediately following your name, headline, and contact information.

Hiring companies want to see what you can actually do with your listed skills. This might include data analysis projects , machine learning projects , and even published scientific articles or coding tutorials . 

Most data science employers will want to look at your project portfolio with an eye for how much regular work you’re doing and what kinds of projects you’re working on. 

“Most of the folks that we interview have their GitHub pages listed on their resume,” says CiBo Technologies talent acquisition manager Jamieson Vasquez. “I think that is important.”

Showcase Relevant Projects

When selecting which projects to highlight on your resume, keep one important factor in mind: relevance. Choose only those projects relevant to the job you’re applying for. Pramp CEO Refael “Rafi” Zikavashvili explains why: 

“Data scientists have one goal, and that is to solve business problems. It’s not about how technically difficult the challenge is, it’s not about how cool the solution is, or the tools that you’re using. It’s about whether you were able to solve business problems. ”

How many projects/publications should you list? As many as you can fit within the one-page parameter. 

Red Flag: Data Science Resumes with No Projects or Publications

Resumes without any projects or publications will definitely send up a red flag for hiring managers.This void will not go unnoticed. Instead, it will highlight your inexperience and make recruiters wonder why you even bothered to apply! 

Need help putting together projects for your resume and portfolio? We have a whole series of blog posts to guide you through building great data science projects. Plus, the next chapter in this guide discusses what projects you should showcase in a job application and how.

Highlight Your Skills

Be as specific as possible about the skills, tools, and technologies you used in each project. Specify the coding language, any libraries you used, etc. Talk about how you created the project, and in the case of group projects, point out your individual contribution. 

Pro-Tip: Redundancy Is Okay! 

Feel like you’re repeating the same skills in the projects section as you plan to list in your skills section? No worries. In fact, the more times you can add those key tools, technologies, and skills in your resume, the better.

Why? Recruiters and hiring managers often use simple keyword searches to scan resumes. So, you want your relevant skills highlighted in as many spots as possible!

Emphasize Communication Skills

Data science recruiters are looking for people who have the technical skills that they need, sure. But they also want people who are effective communicators and who understand the big picture. They want data scientists who can effectively tell stories with data.

One way you can demonstrate these traits is by highlighting collaborative projects. This proves you can work and communicate with a team. 

Another way is by framing your accomplishments in the context of business metrics. This shows you understand how your analyses apply to the bigger business problems you’re trying to solve.

Write your projects and work experience sections with these ideas in mind.

Make Your Projects Stand Out

There will likely be many applications for the job you want. How can you give your application that competitive edge it needs? Here are two more strategies to consider. 

Mention Unstructured Data

That is, any data you’ve worked with that required you to build spreadsheets/data tables yourself.

Examples of this could be working with videos, posts, blogs, customer reviews, and audio. Experience working with unstructured data is impressive. It shows you’re capable of doing unique work with messy data, not just crunching numbers in pristine datasets.

Identify Measurable Results

“If you want to take your resume from good to great, make sure you list measurable achievements,” says Zety.com recruiter Ewa Zakrzewska.

For example, if you created a machine learning model that would improve sales targeting by 15% as one of your projects, say that! 

“‘This is the thing I was trying to do, this is what I did, and these are the results.’ Laying projects out like that really creates a powerful resume,” says Michael Hupp, data science and analytics manager at G2 Crowd .

Here’s a sample of what this section of your resume might look like:

Step #6: Detail Your Relevant Work Experience

Next comes your work experience. Your most recent work experience should be listed on top, with the preceding job below that, and so on in chronological order.

How far should you go back? That depends. Five years is usually the cap, but if you have relevant work experience that goes back further than that, you may want to include it.

Resume Red Flag: Gaps in Work History

Keep in mind that gaps of longer than six months in your work experience section are a major red flag for recruiters and hiring managers. If you have such a gap, you most definitely want to explain it on your resume.

For example, if you took two years off to raise children between 2015 and 2017, you still want to add those dates on your resume. Simply state that you were a stay-at-home parent during that period.

Writing the Job Entries

When writing this section, each entry should include the following: 

• your job title
• the company
• the period of time you held the position
• your accomplishments in that role

If you have relevant work experience to the job you’re applying for, make sure your description consists of mostly accomplishments rather than duties. Employers want to see what you actually did, not just what you were supposed to do.

If your work experience is not relevant to the job you’re applying for, then you’ll still want to list it. But, you only need to include a company name, your job title, and the dates worked. You don’t need to take up space with all the details of an irrelevant job.

Here’s an example of what you might include for a relevant job:

Step #7: List Your Education

Many resume templates list education first. But if you’ve got work experience and/or relevant projects to showcase, you’ll want to show those off first and put education closer to the bottom.

List only post-secondary degrees (i.e., community college, college, and graduate degrees). If you went to college but did not receive a degree, it is best not to list that school. 

What if your degree is not relevant to the job you’re applying for? You should still list it. Some positions simply require a degree in any field, so you want to ensure you’re in the running for these positions.

Pro-Tip: Don’t Forget Your Data Science Certificates! 

Finally, list relevant “micro-degrees,” online training certifications, and other professional training here. 

Data science certificates like those offered through Dataquest are great to add here. That’s because they can show recruiters targeted skills. Plus, each Dataquest course includes an opportunity to create skills-based projects that you can also include on your resume! 

Resume Red Flags: Older Degrees and High School Diplomas

If the graduation date for your degree is 15+ years back, use your discretion about whether you want to include a date or not. Unfortunately, some companies see a graduation date starting with 19XX as a red flag.If you don’t have a degree, don’t sweat it. Just leave the Education section completely off of your resume. What you don’t want to do is list your high school information. This is another red flag for recruiters and hiring managers.

Step #8: Add Skills and Extras

There are a couple more ways you can show off your skills in addition to listing your data science projects and publications:

• Include the relevant skills you have learned in a “Skills” section.
• Add an “Extras” section with relevant activities and training.

The Skills Section Is Not Optional!

Recruiters and hiring managers will most likely do a keyword search as a first step in viewing your resume. You want to make sure key terms like “Python” or “machine learning” are highlighted. 

Recruiters assume that the skills you list first are your strongest skills , and the skills you list last are your weakest. For that reason, list your strongest and most relevant skills first. Leave skills where you’re less comfortable or that are less likely to be relevant to the position for later in your list.

Resume Red Flag: Skills You Can’t Explain

You want to be careful not to go overboard here.

“I think a huge red flag is putting too many technologies on [a resume] and then not being able to back them up, especially in a phone call,” says Clay McLeod, manager of Bioinformatics Software Development at St. Jude Children’s Research Hospital .

Stephanie Leuck, a university recruiter at 84.51° , sees thousands of entry-level data science resumes a year. She echoed this sentiment. “Make sure [the skills you list on a resume] are skills that you can actually speak to. If you read a book once about R, but you can’t actually code in R and you’ve never coded in R, don’t list R as one of your skills. Only put skills on there that you can speak to.”

Pro-Tip: Don’t List Soft Skills

Should you include soft skills here? Probably not. Recruiters tend to scan this section looking for the specific technical skills they need. Stating that you’re skilled in “communication” or a “team player” isn’t going to help your resume stand out.

It’s better to show that you have these skills in the project and work experience sections. Here, you can highlight the ways your “soft” and “hard” skills have functioned together to produce meaningful results.

When to Add an Extras Section

If you have the room, consider including an “Extras” section. This section can be labeled Awards, Certifications, or Training, for instance.

In the data science realm, you have a few options for this section. For example, you might want to list any good Kaggle competition results you’ve had or relevant meetups/events you’ve participated in. Anything else that demonstrates you’re actively involved in learning and doing data science is also fair game.

Data science and machine learning hackathons like Machinehack and Hackerearth are a huge plus on your resume. It shows you have a healthy competitive spirit. Plus, it lets recruiters know you can enhance your skills and knowledge while creating actual content and projects.

Here’s a sample of what the skills and extras sections might look like:

Polish With Finishing Touches

Once you’re finished adding all of the relevant content to your resume, the last major thing to do is a spelling and grammar check . A huge red flag for recruiters and hiring managers is having grammatical or spelling errors on your resume.

Resume Red Flag: Errors and Typos

Remember, recruiters often get hundreds or thousands of applications for entry-level jobs. They’re often looking for any excuse possible to weed out candidates!

Although it might seem minor, a simple typo suggests a lack of attention to detail. Believe it or not, that’s enough for some recruiters to toss out your resume regardless of the skills and experience you have.

So make sure the writing is free of errors and everything is phrased simply and clearly. Having an editor friend give your resume a check is always a good idea, but apps like Hemingway and Grammarly can also help you clean up and simplify your writing.

Pro-Tip: Use a Human Editor

Be careful not to put too much trust in automated grammar-check software. Even the best apps make mistakes.

A finished data science resume might look something like this:

Of course, a resume doesn’t mean much if you can’t prove you’ve got the skills it lists. In the next chapter , we’re going to take a deeper look at what kinds of projects you should be doing, and how you should be highlighting them in your portfolio.

This article is part of our in-depth Data Science Career Guide.

• Introduction and Table of Contents
• Before You Apply: Considering Your Options
• How and Where to Find Data Science Jobs
• How to Write a Data Science Resume — You are here.
• How to Create a Data Science Project Portfolio
• How to Fill in Application Forms, When to Apply, and Other Considerations
• Preparing for Job Interviews in Data Science
• Assessing and Negotiating Job Offers

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Data Scientist Resume - Sample & Guide for 2024

You’re a data scientist. You solve complex problems.

Your newest problem: writing a resume for that elusive data scientist role.

Fortunately, you’ve arrived at the best place. This guide will take you through a range of steps, so you can create a data scientist resume that gets results. 

• An example of a finished data scientist resume that works
• How to write a data scientist resume that’ll fill up your interview diary
• How to make your data scientist resume stand out [with top tips & tricks]

Before we get stuck into the data, here’s a data scientist resume example, created with our very own online resume builder :

This resume performs as well as it looks. Just follow the steps in this guide to create a data scientist resume that gets great results, just like the above example.

Besides our data scientist resume example, we've got even more resume examples for professionals in the computer science field:

• Data Analyst Resume
• Data Entry Resume
• Computer Science Resume
• Artificial Intelligence Engineer Resume
• Engineering Resume
• Software Engineer Resume
• Web Developer Resume
• Java Developer Resume

How to Format a Data Scientist Resume

Before you can reveal why you’re the best person for the job, you need to pick the best format.

Now, this is more important than it sounds.

It will allow your best attributes to ‘jump off the page’ into the recruiters' vision. 

The most common resume format is “ reverse-chronological ”, and it’s for good reason. Essentially, it allows the recruiter to immediately see the value that you provide. We recommend the majority of individuals start with this format.

The following resume formats also get our approval:

• Functional Resume – If you have strong skills, but a weak work history, then this resume format is recommended. It’s ideal for skilled scientists that don’t have a lot of experience or have gaps in their employment history
• Combination Resume – Acting as a combination of both the “Functional” and “Reverse-Chronological” formats, you can use a combination resume if you have a wealth of work experience

Once you’ve chosen your format, you need to organize your resume layout .

Use a Data Scientist Resume Template

As a data scientist, you present data in a structured way.

The same needs to happen to your resume.

However, creating a structured file isn’t an easy task!

You could use Word, but then you will have to risk the layout falling apart with every small alternation. 

Want to skip formatting issues? Use a data scientist resume template .

What to Include in a Data Scientist Resume

The main sections in a data scientist resume are:

• Work Experience
• Contact Information

Want to go a step further? You can also add these optional sections:

• Awards & Certification

Interests & Hobbies

What should you write for each section? 

Read on to learn how.

Want to know more about resume sections? View our guide on What to Put on a Resume .

How to Correctly Display your Contact Information

Now, there is no need to get creative in this section. 

The only requirement is accuracy. 

An incorrect contact section may mean the recruiter can’t contact you – disaster! 

The contact information section on your resume must include:

• Title – In this case, “Data Scientist”
• Phone Number – Check this multiple times for errors
• Email Address – Use a professional email address ([email protected]), not your childhood email ([email protected]).
• (Optional) Location - Applying for a job abroad? Mention your location.
• Ellie Branning, Data Scientist. 101-358-6095. [email protected]
• Ellie Branning, Data Scientist Whizz. 101-358-6095. [email protected]

How to Write a Data Scientist Resume Summary or Objective

It’s safe to say that recruiter’s don’t have time to dig into the data of every resume.

Instead, they scan the resume for the main points.

In fact, studies have shown that recruiters spend just a few seconds on each resume! 

So, what can you do?

You need an introduction that makes your value ‘jump off the page’.

To do this, use a resume summary or objective .

These are snappy paragraphs that go on top of your resume, just under your contact information. 

Now, this section is extremely important. This small paragraph could be the deciding factor between scoring an interview and simply having your resume dismissed.

But what is the difference between the two sections?

A resume summary is a 2-4 sentence summary of your professional experiences and achievements.

Certified data scientist with 12 years of experience for a diverse clientele. Achievements include updating data streaming processes for an 18% reduction in redundancy, as well as improving the accuracy of predicted prices by 18%. Highly-skilled in data visualization, machine learning, leadership.

A resume objective is a 2-4 sentence snapshot of what you want to achieve professionally.

Motivated data scientist with 2+ years of experience as a freelance data scientist. Passionate about building models that fix problems. Relevant skills include machine learning, problem solving, programming, and creative thinking.

So, which one is best, summary or objective?

Generally, we recommend that experienced data scientists go with a resume summary. Those who are new to the field, like graduates and career changers, would be better suited to an objective. 

How to Make Your Data Scientist Work Experience Stand Out

Recruiters need to be confident that you will do a good job for the company.

Listing your work experience is the easiest and best way to do this.

Here’s the best way to structure your work experience section:

• Position name
• Company Name
• Responsibilities & Achievements

Data Scientist

03/2016 - 05/2019

• Improved the accuracy of predicted prices by 18%.
• Coordinated a team of 16 data scientists working on 4 different projects.
• Updated data streaming processes for a 18% reduction in redundancy.

To separate your resume from the other applicants, you should talk about your best achievements, not your daily tasks. Doing so will clearly show how you can benefit the company.

Instead of saying:

“Data streaming.”

“Updated data streaming processes for an 18% reduction in redundancy.”

As you can see, the first statement doesn’t effectively convey your achievements. It shows that you streamed data, but it doesn’t show the results of your work. 

The second statement shows that you managed to reduce the redundancy numbers. Hard numbers that prove your skills – can’t argue with that!

What if You Don’t Have Work Experience?

Maybe you’re trying to break into the data science field?

Or maybe, you have already worked in the industry, but never in this specific role?

Your experience is null .

A recruiter will want data scientists that they can rely on. Whether you have job experience or not, being able to show that you have the skills is the most important factor.

If you already have proof of your data science skills, feel free to link to them in your resume.

With that said, there is still time to create a portfolio.

Here are several ways you can show your talents (and even get paid for it):

• Start freelancing.
• Offer your skills to friends and family.
• Contribute to open source projects on GitHub.
• If the above doesn’t work, become your own client! Show your skills by creating mock projects.

Are you recent data scientist graduate? Make sure to check out our student resume guide !

Use Action Words to Make Your Data Scientist Resume POP!

…are all common words that the recruiter sees time and time again.

However, you want to separate your resume from the competition, which means using power words to make your achievements stand out:

• Conceptualized
• Spearheaded

How to Correctly List your Education

Every great resume needs an education section.

But don’t worry, there is nothing too complicated here.

Simply enter your education history in the follow format:

• Degree Type & Major
• University Name
• Years Studied
• GPA, Honours, Courses, and anything else you might want to add

BSc in Statistics

University of Bath

2012 - 2016

• Relevant Courses: Probability and Statistics, Generalised Linear Models, Applied Statistics

Now, you may have some questions on this section. If so, here are the answers to some of the most frequent questions that we get:

• What if I haven’t finished education yet?

Regardless of whether you’re a data science graduate or still studying, you should mention all years studied to date

• Should I include my high school education?

The general rule is to only include your highest form of education. So, include your high school education if you don’t have a relevant degree for data science

• What do I put first, my education or experience?

Experiences are the priority, so those go first. If you’re a recent graduate, you will likely need to start with education.

Need to know more? Check out our guide on how to list education on a resume .

Top 15 Skills for a Data Scientist Resume

When it comes to the skills section, the hiring manager has seen it all before.

In fact, they need a data scientist to help with the entire pile of data scientist resumes!

You see, everyone lists all of their skills, even those that related to the job.

Your skill section should highlight your top skills in a way that is specific to the role.

Here are some of the most common data scientist skills:

Hard Skills for a Data Scientist Resume:

• Data Analysis
• Data Visualization
• Quantitative Analysis
• Machine Learning
• Mathematics
• Probability
• Programming

Soft Skills for a Data Scientist Resume:

• Critical Thinking
• Communication
• Time-Management
• Collaboration
• Data scientists frequently use tools, such as Cloudera, PERL, and OpenRefine. If there are any tools or pieces of software that you’re an expert in, include them in your skills section.

Here’s a more comprehensive list of 101+ must-have skills this year .

What Else Can You Include in a Data Scientist Resume?

We’ve now covered every essential resume section .

Is it the absolute BEST it can be?

Doing a great job with the above sections should be enough to get you shortlisted, but adding a few of the following sections could be the major factor in whether you become their new data scientist or not.

Awards & Certifications

Have you won an award for your work in a field that relates to data science?

Have you completed any courses to improve your skills and knowledge?

If you said yes to any of the above, make sure to mention them in your resume!

Don’t worry if you don’t have any awards or certificates, there a few companies that allow users to do online certifications, like Google.

• “IBM Data Science” - Coursera Certificate
• Google Certified Professional Data Engineer – GCP
• Microsoft Professional Program Certificate in Data Science
• “Deep Learning” - Coursera Certificate
• “Critical Thinking Masterclass” - MadeUpUniversity

Even though it is very unlikely to need a second language, you may want to add a small languages section to your resume. 

You see, being able to speak a second language is always an impressive skill to a hiring manager. 

Rank the languages by proficiency:

• Intermediate

Now, you may be wondering, “why would a recruiter need to know about my love for kayaking?”

Well, your hobbies reveal more about who you are as a person.

A hobbies section is an easy way to add personality to your resume, so add one if you have the space.

Here’s which hobbies & interests you may want to mention.

Include a Cover Letter with Your Resume

Here the thing –

Cover letters still play an important role during the application process.

They provide a number of benefits, but the main reason for using a cover letter is to show the recruiter that you care about working for their company.

To create a winning cover letter, we must use the correct structure. 

Here’s what we recommend:

You should complete the following sections:

Personal Contact Information

Your full name, profession, email, phone number, location, and website (or Behance / Dribble).

Hiring Manager’s Contact Information

Full name, position, location, email.

Opening Paragraph

It’s no secret that hiring managers skim through resumes and cover letters. As such, you need to hook the reader within the first few sentences. Use concise language to mention:

• The position you’re applying for
• Your experience summary and best achievement to date

Once you’ve sparked the reader’s interest, you can get deeper into the following specifics:

• Why you chose this specific company
• What you already know about the company
• How your skills relevant for the role
• Which similar industries or positions have you worked in before

Closing Paragraph

Don’t just end the conversation abruptly, you should:

• Conclude the points made in the body paragraph
• Thank the hiring manager for the opportunity
• Finish with a call to action. This is a good way to start a conversation. A simple “At your earliest opportunity, I’d love to discuss more about how I can help company X” will work

Formal Salutations

End the cover letter in a professional manner. Something like “Kind regards” or “Sincerely” will be proficient.

For more inspiration, read our step-by-step guide on how to write a cover letter .

Key Takeaways

If you followed all of the above advice, you’ve given yourself the best possible chance of landing that data scientist role.

Let’s quickly summarize what we’ve learnt:

• Format your data scientist resume correctly by prioritizing the reverse-chronological format and then following the content layout guidelines
• Start your resume with a summary or objective to hook the recruiter
• In your work experience section, give attention to your best achievements, rather than your responsibilities
• Craft a convincing cover letter for an unbeatable application

Suggested Reading:

• How to Ace Interviews with the STAR Method [9+ Examples]
• 22+ Strengths and Weaknesses for Job Interviews
• What Is Your Greatest Accomplishment? [3 Proven Answers]

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Data Scientist Resume Examples For 2024 (20+ Skills & Templates)

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Looking to score a job as a Data Scientist?

You're going to need an awesome resume. This guide is your one-stop-shop for writing a job-winning Data Scientist resume using our proven strategies, skills, templates, and examples.

All of the content in this guide is based on data from coaching thousands of job seekers (just like you!) who went on to land offers at the world's best companies.

If you want to maximize your chances of landing that Data Scientist role, I recommend reading this piece from top to bottom. But if you're just looking for something specific, here's what's included in this guide:

• What To Know About Writing A Job-Winning Data Scientist Resume
• The Best Skills To Include On A Data Scientist Resume

How To Write A Job-Winning Data Scientist Resume Summary

How to write offer-winning data scientist resume bullets.

• 3 Data Scientist Resume Examples

The 8 Best Data Scientist Resume Templates

Here's the step-by-step breakdown:

Data Scientist Resume Overview: What To Know To Write A Resume That Wins More Job Offers

What do companies look for when they're hiring a Data Scientist?

Companies look for candidates with strong technical skills in programming languages like Python or R and experience with data manipulation, statistical analysis, and machine learning models. Companies are also looking for data scientists with problem-solving skills who can obtain actionable insights from complete datasets.

Your resume should show the company that your personality and your experience encompass all these things.

Additionally, there are a few best practices you want to follow to write a job-winning Data Scientist resume:

• Tailor your resume to the job description you are applying for: Tailor your resume for each application, aligning your skills with the specific requirements of each job description.
• Detail previous experiences: Provide detailed descriptions of your roles, emphasizing hard and soft skills related to the job description.
• Bring in your key achievements: Showcase measurable achievements in previous roles and share your best work.
• Highlight your skills:   Highlight your skills in Sales, Marketing, Communication, Customer Experience, and Management.
• Make it visually appealing: Use a professional and clean layout with bullet points for easy readability. Also, ensure formatting and font consistency throughout the resume and limit it to one or two pages.
• Use keywords: Incorporate industry-specific keywords from the job description to pass through applicant tracking systems (ATS) and increase your chances of being noticed by hiring managers.
• Proofread your resume: Thoroughly proofread your resume to eliminate errors (I recommend Hemingway App and Grammarly ). Consider seeking feedback from peers or mentors to ensure clarity and effectiveness!

Let's dive deeper into each of these so you have the exact blueprint you need to see success.

The Best Data Scientist Skills To Include On Your Resume

Keywords are one of the most important factors in your resume. They show employers that your skills align with the role and they also help format your resume for Applicant Tracking Systems (ATS).

If you're not familiar with ATS systems, they are pieces of software used by employers to manage job applications. They scan resumes for keywords and qualifications and make it easier for employers to filter and search for candidates whose qualifications match the role.

If you want to win more interviews and job offers, you need to have a keyword-optimized resume. There are two ways to find the right keywords:

1. Leverage The 20 Best Data Scientist Keywords

The first is to leverage our list of the best keywords and skills for a Data Scientist resume.

These keywords were selected from an analysis of real Data Scientist job descriptions sourced from actual job boards. Here they are:

• Data Science
• Communication
• Machine Learning
• Engineering
• Cross-Functional
• Organization
• Collaboration
• Descision Making

2. Use ResyMatch.io To Find The Best Keywords That Are Specific To Your Resume And Target Role

The second method is the one I recommend because it's personalized to your specific resume and target job.

This process lets you find the exact keywords that your resume is missing when compared to the individual role you're applying for.

Here's how it works:

• Open a copy of your updated Data Scientist resume
• Open a copy of your target Data Scientist job description
• In the widget below, paste your resume on the left, paste the job description on the right, and hit scan!

ResyMatch is going to scan your resume and compare it to the target job description. It's going to show you the exact keywords and skills you're missing as well as share other feedback you can use to improve your resume.

If you're ready to get started, use the widget below to run your first scan and get your free resume score:

Copy/paste or upload your resume here:

Click here to paste text

Upload a PDF, Word Doc, or TXT File

Paste the job post's details here:

Scan to compare and score your resume vs the job's description.

Scanning...

And if you're a visual learner, here's a video walking through the entire process so you can follow along:

Employers spend an average of six seconds reading your resume.

If you want to win more interviews and offers, you need to make that time count. That starts with hitting the reader with the exact information they're looking for right at the top of your resume.

Unfortunately, traditional resume advice like Summaries and Objectives don't accomplish that goal. If you want to win in today's market, you need a modern approach. I like to use something I can a “Highlight Reel,” here's how it works.

Highlight Reels: A Proven Way To Start Your Resume And Win More Jobs

The Highlight Reel is exactly what it sounds like.

It's a section at the top of your resume that allows you to pick and choose the best and most relevant experience to feature right at the top of your resume.

It's essentially a highlight reel of your career as it relates to this specific role! I like to think about it as the SportsCenter Top 10 of your resume.

The Highlight Reel resume summary consists of 4 parts:

• A relevant section title that ties your experience to the role
• An introductory bullet that summarizes your experience and high-level value
• A few supporting “Case Study” bullets that illustrate specific results, projects, and relevant experience
• A closing “Extracurricular” bullet to round out your candidacy

For example, if we were writing a Highlight Reel for a Data Scientist role, it might look like this:

The first bullet includes the candidate's years of experience in the role and wraps up with a value-driven pitch about how they've helped companies in the past.

The next two bullets are “Case Studies” of specific results they drove at their company. The last bullet wraps up with extracurricular information.

This candidate has provided all of the info any employer would want to see right at the very top of their resume! The best part is that they can customize this section for each and every role they apply for to maximize the relevance of their experience.

Here's one more example of a Data Scientist Highlight Reel:

The content of this example showcases a candidate transitioning from sales to data science, leveraging their experience with sales and bringing in measurable results in each bullet point. Then, they wrap up with a high-value extracurricular activity that's related to their target position.

If you want more details on writing a killer Highlight Reel, check out my full guide on Highlight Reels here.

Bullets make up the majority of the content in your resume. If you want to win, you need to know how to write bullets that are compelling and value-driven.

Unfortunately, way too many job seekers aren't good at this. They use fluffy, buzzword-fill language and they only talk about the actions that they took rather than the results and outcomes those actions created.

If you apply this framework to each of the bullets on your resume, you're going to make them more compelling and your value is going to be crystal clear to the reader. For example, take a look at these resume bullets:

❌ Data Scientist with 5+ years of experience.

✅ Leveraging 5+ years of experience in data science, specializing in predictive modeling to improve decision-making accuracy by 40%.

The second bullet makes the candidate's value  so much more clear, and it's a lot more fun to read! That's what we're going for here.

That said, it's one thing to look at the graphic above and try to apply the abstract concept of “35% hard skills” to your bullet. We wanted to make things easy, so we created a tool called ResyBullet.io that will actually give your resume bullet a score and show you how to improve it.

Using ResyBullet To Write Crazy Effective, Job-Winning Resume Bullets

ResyBullet takes our proprietary “resume bullet formula” and layers it into a tool that's super simple to use. Here's how it works:

• Head over to ResyBullet.io
• Copy a bullet from your resume and paste it into the tool, then hit “Analyze”
• ResyBullet will score your resume bullet and show you exactly what you need to improve
• You edit your bullet with the recommended changes and scan it again
• Rinse and repeat until you get a score of 60+
• Move on to the next bullet in your resume

Let's take a look at how this works for the two resume bullet examples I shared above:

First, we had, “Data Scientist with 5+ years of experience.” 

ResyBullet gave that a score of 35/100.  Not only is it too short, but it's missing relevant skills, compelling language, and measurable outcomes:

Now, let's take a look at our second bullet,  “Leveraging 5+ years of experience in data science, specializing in predictive modeling to improve decision-making accuracy by 40%”.

ResyBullet gave that a 61 / 100. Much better! This bullet had more content focused on the experience in the Data Scientist role, while also highlighting measurable results:

Now all you have to do is run each of your bullets through ResyBullet, make the suggested updates, and your resume is going to be jam-packed with eye-popping, value-driven content!

If you're ready, grab a bullet from your resume, paste it into the widget below, and hit scan to get your first resume bullet score and analysis:

Free Resume Bullet Analyzer

Learn to write crazy effective resume bullets that grab attention, illustrate value, and actually get results., copy and paste your resume bullet to begin analysis:, 3 data scientist resume examples for 2024.

Now let's take a look at all of these best practices in action. Here are three resume examples for different situations from people with different backgrounds:

Data Scientist Resume Example #1: A Traditional Background

Data Scientist Resume Example #2: A Non-Traditional Background

For our second Data Scientist Resume Example, we have a candidate who has a non-traditional background. In this case, they come from a background in sales but leverage experiences that have helped them transition to a Data Scientist role. Here's an example of what their resume might look like:

Data Scientist Resume Example #3: Data Scientist New Grad

For our third Data Scientist Resume Example, we have a new graduate who's never worked for a company before but has worked on several self-initiated projects. Here's an example of what their resume might look like when applying for Data Scientist roles:

At this point, you know all of the basics you'll need to write a Data Scientist resume that wins you more interviews and offers. The only thing left is to take all of that information and apply it to a template that's going to help you get results.

We made that easy with our ResyBuild tool . It has 8 proven templates that were created with the help of recruiters and hiring managers at the world's best companies. These templates also bake in thousands of data points we have from the job seekers in our audience who have used them to land job offers.

Just click any of the templates below to start building your resume using proven, recruiter-approved templates:

Free Job-Winning Resume Templates, Build Yours In No Time .

Choose a resume template below to get started:.

Key Takeaways To Wrap Up Your Job-Winning Data Scientist Resume

You made it! We packed a lot of information into this post so I wanted to distill the key points for you and lay out next steps so you know exactly where to from here.

Here are the 5 steps for writing a job-winning Data Scientist resume:

• Start with a proven resume template from ResyBuild.io
• Use ResyMatch.io to find the right keywords and optimize your resume for each role you apply to
• Open your resume with a Highlight Reel to immediately grab your target employer's attention
• Use ResyBullet.io to craft compelling, value-driven bullets that pop off the page
• Compare the draft of your resume to the examples on this page to make sure you're on the right path
• Use a tool like HemingwayApp or Grammarly to proofread your resume before you submit it

If you follow those steps, you're going to be well on your way to landing more Data Scientist interviews and job offers.

Now that your resume is taken care of, check out my guide on how to get a job anywhere without applying online!

Paula Martins

Paula is Cultivated Culture's amazing Editor and Content Manager. Her background is in journalism and she's transitioned from roles in education, to tech, to finance, and more. She blends her journalism background with her job search experience to share advice aimed at helping people like you land jobs they love without applying online.

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9 PhD Resume Examples & Guide for 2024

Your PhD resume must highlight your extensive research and expertise in your field of study. Tailor it to showcase how your unique skills are transferable to the job you're applying for. Demonstrate your proficiency in critical thinking and problem-solving through concrete examples of your work. Articulate your ability to communicate complex ideas effectively, as this is key to standing out.

All resume examples in this guide

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Resume Guide

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Systematizing Your Experience

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Ph.D. in Progress on Resume

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Key Takeaways

George C. Jones's 8-year-Odyssey to his doctoral degree in engineering is finally complete. All he has to do now is complete his Ph.D. resume.

Little does George know that within the next three years, he'd apply to 500+ roles and still not be any closer to working in the renewable energy sector.

A little birdie told us why.

Everywhere George applied, he was labeled as an "egghead": book-smart with no real-world experience.

At least that's the story his resume told.

Unfortunately, George's sad "tale" depicts many graduates’ job searches.

By 2030/31, the US is expected to have 221,000 Ph.D. graduates . While this may be fantastic news for the academic community, the job pool is limited.

Swimming into the big blue sea - a.k.a. the "real world" - would require Ph.D. graduates to adapt their resumes to the business or industry requirements.

And listing vague bullets pinpointing your experience just won't do.

Enhancv knows how to translate your skills, knowledge, and achievements to ensure your resume stands out in the vast talent pool.

Because a well-written Ph.D. resume , showing skills, passion, and knowledge, is your ticket to the moon and beyond.

Our exclusive, Ph.D. resume guide will answer some of the following questions for you:

• What are some of the biggest, unintentional mistakes Ph.D. graduates tend to make when writing their resumes?
• Perfecting the top one-third of your resume: how to get recruiters' attention every time?
• 102 of the most popular Ph.D. skills you need to add to your resume right now!
• Lacking much professional experience: how to align your academic background with job expectations?
• Get inspired with more ideas about formatting, writing your resume summary, and including your academic body of work in a way that works.

4 additional Ph.D. resume samples and why they work

Ph.d. bioengineering graduate.

This Ph.D. Bioengineering Graduate has certainly focused her effort on getting that tenure-track position.

Here’s why her resume works.

Within the top one-third of Dr. Taylor’s resume (the headlines, summary, and skills section), you’ll find many relevant keywords for the job advert.

She has also used the summary to qualify and quantify her results to help recruiters better understand her strengths.

The experience section is split into two parts: the first one notes all relevant teaching experience; the second section showcases depth and knowledge of research.

Dr. Taylor has included industry experience and strengths to further define her skill set and show results.

This approach would also be very helpful for any Applicant Tracking System (ATS) reviewing the resume, as it cross-aligns the candidate’s talents with the job requirements.

Ph.D. mechanical engineering

If you’re wondering how to translate your academics into real-life experience, check this example out.

Dr. Aubry’s resume headline highlights his area of expertise and his Ph.D. degree, while his summary qualifies his key academic achievements, contributions, and success.

Dr. Aubry’s skills section focuses more on the technology he is apt at within the specified job requirements.

His roles as a lab technician, contributor, and research assistant also hint to recruiters that he’s results-oriented and can show how his work affected the industry.

Finally, his strengths section cross-aligns soft skills that are important for the role, including mechanical engineering knowledge, cross-disciplinary teamwork, and technical writing.

Apta certified physical therapist

In some industries, a specific certification can be a real game changer to meeting ATS standards.

Dr. Brubaker knows this and that’s why she has used her resume to highlight her American Physical Therapy Association (APTA) certification within various sections (headline, summary, and certification).

Another reason why Dr. Brubaker’s resume works is that it pinpoints her niche of expertise with tangible results.

Her education section not only lists all of her degrees, but she has turned recruiters’ attention to the grant funding her Ph.D. thesis secured (an outcome of the project).

Did you notice how she curated the experience section? Dr. Brubaker started with her more job-oriented responsibilities, followed by a leadership role.

if you’ve participated in any extracurricular activities during your studies, they could indicate various soft skills on your resume, including leadership, initiative, organization, etc.

data scientist, ongoing Ph.D. in statistics

Are you a current Ph.D. student, wondering how to include your ongoing degree on your resume?

Charles Flack’s resume includes some of the best how-to practices.

Charles uses the resume headline and summary’s first section to specify his research niche, which is followed by his ongoing Ph.D. Status.

N.B. Remember that the recruitment process is one of building trust with honesty. If you note in your resume that you’re a Ph.D. graduate (without this being the reality), recruiters will find out.

Continuing with Charles’s resume summary. It goes on to include an array of expertise and soft skills (e.g. leadership, adaptiveness, perfectionism) all within achieved results in academia.

His experience section includes roles in leadership, technical writing, and private tutoring, denoting individual skills and contributions.

The strengths Charles has included are more specific and tie in with data science roles. Those include quantitative problem-solving, deep learning, and iterative process.

21 Ph.D. related resume examples to help you get inspired

• Entry-Level
• Grant Writer
• Lab Technician
• Research Associate
• Research Assistant
• Lab Assistant
• Lab Manager
• Finance Intern
• Data Science Intern
• Entry Level Engineering
• Entry-Level Mechanical Engineer
• Software Engineer Intern
• Nursing Student
• Data Analyst Entry Level

Quick steps to success in writing your Ph.D. resume to get recruiters’ attention

Let's start with a big no-no: your academic CV, the one you used to secure that tenure position, is a No-go. Put simply - it lacks personality .

HRs and the Applicant Tracking System (ATS) need more context to your experience.

Unfortunately, here comes one of the biggest disadvantages you may face, leaving academia. Often, Ph.D. graduates get rejected as they lack practical work experience .

Don't get discouraged. Instead, find a resume format that works for you.

For Ph.D. graduates that have less work experience, we recommend a functional-skill-based format . It will help you highlight your unique skill set and academic excellence.

Also, it'll align your niche area of expertise with the role expectations.

Now that we've settled the formatting debate, let's look at a couple of more quick pointers for your Ph.D. resume.

1. The top one-third of your resume - the resume header and summary - is crucial to getting a high score on the ATS.

That's why you should try to include as many of the advert's relevant requirements within this section.

2. Expand your qualifications and skills within the experience section.

Don't just list plain bullets, but focus on the outcomes of your studies, research, or publications.

How to write your experience bullets:

3. Speaking of impact, detail your accomplishments within your academic work.

Focus on the picture and your research's influence on the scientific field, business/ industry, or communities.

Bonus: Remember to always list all work and academic experience that is relevant to the job you’re applying for.

Your Ph.D. experience can open many doors for you, giving you a front-row seat on the cutting edge of new technologies.

But let's not get ahead of ourselves, here are a couple more bits and pieces to keep in mind when writing your Ph.D. resume.

What recruiters are looking out for in your Ph.D. resume:

• What methodology or technology have you used to prove your research?
• If you've ever led teams, were you able to manage them successfully?
• Would your niche area of expertise contribute to the organizational goals?
• Can you bring to the table more than just theoretical knowledge?
• How fast can you adapt to a non-academic environment and deliver tangible results?

Ph.D. resume's five most important sections:

• Resume header with keywords from the job description
• Resume summary cross-aligning requirements with experience
• Resume experience to expand on the summary
• Education section, listing all diplomas
• Professional achievements in research and publications

Your Ph.D. resume should balance your knowledge with how fast it can be applied in a real-world environment.

That's why you need to be precise about the resume sections you chose to prove your merit.

Mythbusters: Your PhD resume header under the Enhancv microscope

After endless hours of searching different platforms, you've finally found that cancer research position that perfectly matches your profile.

Avoiding all emotional attachment and excitement, you finally decide on the following header:

2 PhD resume header examples

Let’s look at the bright side of things. If the organization would like to get in touch with Dr. Garnett, they'd easily find his contact details .

But on the other hand, he is making one huge mistake: Dr. Garnett isn't taking advantage of the power of the top section of his resume. More specifically - his headline .

This crucial section could provide Ph.D. graduates with an opportunity to include all relevant keywords that could match their profile. And at the same time, tease their professional story.

This may be obvious, but this example works as it shows that Dr. Garnett is not only a Ph.D. graduate, but his specific area of research and interest, which should supposedly match with the role he’s applying for.

A rule of thumb for headers is to never be vague about your research and expertise.

You could list your Ph.D. degree within your resume title so that it’s the first thing recruiters (and the ATS) see.

In the case of Dr. Garnett, his resume title could read “Dr. David Garnett, Ph.D.”.

Our suggestion is to be wary about the organization you’re applying for because if the culture is more informal, this may come off as “pretentious”.

Ph.D. resume summaries: HRs’ favorite instrument for advanced career storytelling

The Ph.D. resume summary is a really useful section for good first impressions and explaining your experience.

The summary can be used to highlight your skills, strengths, and achievements. While telling the story of your professional growth.

We know how important real-world examples are for you.

So, without further ado, here's how Dr. Lucina Collard rewrote her resume summary. And in the end, got the attention of a prestigious software development company.

2 PhD resume summary examples

Dr. Collard may have spent too much time in the lab, as her Ph.D. resume summary just lists what courses she took and her thesis statement.

No results, no outcomes.

In the end, she did decide to include some soft skills and passions, but without actually pointing out the “why” behind her work. This doesn’t make sense at all.

Here’s what her modified resume summary looked like in the end:

This summary works for one simple reason: it qualifies the achievements.

Dr. Collard has noted that within the past 6 years, she has been specializing in the job advert keywords “robotics” and “mechatronics”. The award is also a nice touch to paint the big picture of her experience.

Dr. Collard is apt at achieving results within a dynamic environment. That includes various professionals from different backgrounds.

When talking about her thesis, she goes on to show her familiarity with the process.

Editing Dr. Collard’s summary may seem just like one small step for her, but it’s actually a giant leap to securing an interview.

Making your Ph.D. resume experience section stand out for all the right reasons

When listing their experience section, most Ph.D. graduates are probably making the same mistakes.

The first one: leaving out your academic practice, thinking that recruiters only want to see work experience.

That's not true at all.

Your education would not only prove your technical capabilities, but also your soft skills. But, more on that in the following paragraphs.

The second error: those tricky job titles.

You'd find 1000+ resumes, listing each experience using the given academic titles; e.g. Professor, Lecturer, Post Doctorate Student, Graduate Student, etc.

Robotic vs personalized approach: 1:0.

The ATS, reviewing your resume, is set to recognize keywords that are vital for the job. The faster those appear at the top of your experience, the better.

Instead of listing that you used to be a "Lecturer at XYZ University", go with "Data Science and Machine Learning Lecturer at XYZ University".

Third slip-up: those tricky experience bullets.

Some Ph.D. graduates just list all their courses, research, and publications.

This isn't the way to go.

You should rather align your knowledge with the job description to prove tangible results.

A couple of questions to help:

• What did this course help me learn and achieve that could be applicable to the job I'm applying for?
• How did leading lectures help me to cooperate better within the learning environment?
• What grants did my publications secure for the educational institute?
• What effects did my research have on the big-picture subjects within the field?
• The more you can get into the actuality (and practicality) of your education, the higher your chances are to get your first interview booked.

Let’s look at an example of how your experience can be showcased within your resume as crucial for the job.

Phd resume experience examples.

• • Got PhD in Philosophy
• • Took Philosophy of Mind, Brain, and Behavior Course
• • Took Ethics Course
• • Wrote Diploma on ‘To Be Or Not To Be: The Ethics of The Human Existence In The 21st Century’

This experience section is pretty negligent and robotic. Did you just get a diploma during those three-plus years?

You may be exiting from a leading higher education institution, but putting in the extra effort to your resume shows that you are diligent.

And that you’re actually invested in getting that particular job.

• • Contributed 60+ publications to the university scientific journal, niching within human rights, ethics, and the big why of human existence
• • Peer-reviewed publications for 12+ philosophy colleagues and professionals with a focus on consistency and validity of the thesis
• • Collaborated with 10+ professionals from arts, science, and biotech fields to question and understand the ethics behind their projects
• • Apart from the scientific paper for my final thesis, entitled 'To Be Or Not To Be: The Ethics of The Human Existence In The 21st Century’, published a short video on the university website to help inspire young professionals of philosophy to always stay alert

There’s no ground for comparison between the two examples. But let’s look at some of the basics.

The first experience bullet hints that the professional can write technical papers for their niche.

Next, the candidate showcases an eye for detail, collaboration, and teamwork.

Finally, they have found a way to get their thesis submitted on time and also make it more understandable.

On a side note - did you notice how a better job title could be a complete game-changer?

One bonus tip on better systematizing your Ph.D. experience

General practice is that you'd create one single resume experience section.

But what if while writing your Ph.D. resume, you realize that in the past six years, all your experience is for the same institution?

Here's an idea to spice up your experience section. You can create a couple of experience sections, based on functionality.

Thus highlighting job advert keywords and, at the same time, including more details.

So you could have some of the following headers, under which you could classify your work:

• Research Experience
• Technical Experience
• Analytical Experience
• Leadership Experience
• Mentorship Experience
• Teamwork Experience
• Higher Education Experience

Feel free to align your transferable skills, which would be beneficial for the job you're applying for.

How should you include your degrees within your Ph.D. resume education section?

Here's the advice you've probably been waiting for; introducing…

… "How to write about your degree without sounding like a snob?"

And there are two possible scenarios at play.

The first is that you're applying for a job related to your area of study.

You should list your Ph.D. degree in detail, including research topics, method expertise, and publications.

As you're writing for non-specialized audiences, don't go overboard with the complex terms. Instead, weave keywords from the job requirements within your education section.

In the second case scenario, you're applying for a job that has nothing to do with your degree.

Keep your education section plain and simple with your degree, university/college, dates, and location.

Either way, remember to always list all of your degrees in chronological order, starting with the latest.

This isn't just some made-up rule or HR caprice. Your resume education helps recruiters determine if:

• Your basic training and knowledge would fit the job
• You stayed focused on your coursework and graduated on time
• You would be a good fit for the team. Some companies tend to hire graduates from the same university

Ph.D. in progress: Should you include your potential degree on your resume?

Being transparent on your Ph.D. resume is what builds that fantastic initial relationship with the company you're applying for.

Thus, you have to be very clear and precise, especially in your education section.

If you're still pursuing your Ph.D. degree, shift the focus from the future to what you've achieved so far.

Your education section could answer any of the following questions:

• How applicable your degree is to the job opening?
• Which of the courses you've completed would help the company grow?
• Is your education a stepping stone within your professional experience?
• What is your expected graduation date?

Being on the course to completing your Ph.D. is definitely commendable, but sometimes life happens. And you may be forced to drop out of your Ph.D. education.

Should you then list the degree you didn't complete?

The answer is 100% yes, as your Ph.D.:

• fills gaps within your professional experience
• is valuable experience
• has helped you gain new knowledge

Making it clear to recruiters that your degree is "Incomplete" or that you "Didn't Graduate" is very important.

List your degree, dates, university/college, and status.

If you get to the interview stage, recruiters will ask you why you dropped out. Be prepared to talk about why it wasn't the best option for your career at the time, or hint at the circumstances.

Even if it's hard to believe, HR managers are people - just like you and me - and they are able to show understanding and compassion.

Ph.D. resume: Is there a dream skill set your potential employers would like to see?

Recruiters review your resume to see how your experience aligns with the role, with a big focus on transferrable skills.

Or in other words, what else can you bring to the table to help the business or institution grow?

And transferable skills can be both hard (or technical ) and soft skills .

Your hard skills include the technology you used to complete your studies.

Consider the opportunities you've has to:

• test and measure antennas parameters in an Anechoic chamber
• audit in a lab environment renewable energy sources' efficiency
• develop software, using Python, to patch cybersecurity risks

The list can go and on and on. Your Ph.D. has probably provided you with a pretty solid technical background.

When writing your resume’s separate technical skills section, ever wonder which technology should go first?

Rule of thumb: align the technology within the job description with your expertise.

The more proficient you are at a certain skill, the sooner you should list it.

Wondering what some of the most popular Ph.D. resume hard and technical skills are?

Check out our list, based on some of the most popular industries.

PhD resume technical skills for various roles:

15 hard skills for opportunities in business consulting:

• Knowledge of different business-crucial frameworks, including Benchmarking, Balanced Scorecard, Porter’s Five Forces, The GE-McKinsey Nine-Box Matrix, The BCG Growth-Share Matrix, Core Competencies
• Data Management and Analysis
• Advanced Data Modelling
• Strategy, Planning and Implementation
• Assessing and Managing Risk Using Frameworks
• Statistics and Understanding Correlations
• CRMs: Salesforce, Zendesk, Bitrix24, etc.
• Lead Generation Software: Zendesk Sell, Pipedrive, HubSpot, etc.
• Project Management Software: Jira, Hive, Asana, etc.
• Employer and Customer Satisfaction Surveys
• Proposal Writing
• Scheduling Software: Calendly, Google Calendar, Doodle, etc.
• Revenue Optimization and Sales

15 technical skills for biology, biotech, biochemistry, and medical research:

• Design, conduct, and analyze scientific research
• Tissue Culture
• PCR (Polymerase Chain Reaction)
• Gel Electrophoresis
• Western Blot
• Molecular (Gene) Cloning and various techniques
• Flow Cytometry
• Mass Spectrometry
• Confocal Microscopy
• Cell-Based Assays
• Radioimmunoassays
• Data Analysis in biotechnology, bioinformatics, and medical research
• Laboratory and Equipment

15 engineering technical skills to add to your PhD resume:

• Manufacturing: Forging, Welding, Assembling, etc.
• Quality Control
• Industrial /System Design and Analysis
• Conceptual, Logical, or Physical Data Modeling
• AI and/ or Machine Learning
• Design Tools: AutoCAD, SolidWork, 3dsMax, etc.
• Programming Languages: C++, Python, Java, etc.
• Equipment Diagnosis
• Project Management: Trello, Zoho, Microsoft Project, etc.
• Data Analysis Software: Microsoft Power BI, Tableau, Qlik Sense, etc.
• CNC Programming
• Advanced Physics
• Structural Analysis
• Nanotechnology

15 recommended computer science technologies:

• Programming languages: C++, PHP, Swift, etc.
• Software engineering and development: Atom, GitHub, Chrome DevTools. etc.
• Cloud Platforms
• Data migration and deployment
• Application Programming Interfaces (APIs)
• Integrated Environments Management
• Network Maintenance
• Cybersecurity
• Machine learning AI
• Business Intelligence and Statistical Analysis Tools
• SQL Consoles
• SAS Development and Forecasting
• Data Modelling Tools: ER/Studio, Archi, Ludichart, etc.
• Automation Tools

15 academic and research technical skills:

• Technical Literacy
• Presentation and visual: Tableau, Prezi, PPT, etc.
• Learning platforms: Moodle, Classroom, Teams, etc.
• Surveys: Google Forms, MailChimp, Kahoot, etc.
• Data-Processing Software: SPSS, RStudio, NVivo, etc.
• Academic Networks: Google Scholar, Academia.edu., ResearchGate, etc.
• Academic Research and Technical Writing
• Email Writing
• Data and Information Analysis
• Copyright and License
• Videoconferencing: Zoom, Teams, Google Meet, etc.
• Applications for Securing Grants and Funding
• Peer Reviews and Co-Writing Interdisciplinary Technical Papers

Moving on to your PhD resume soft skills

There's still no precise formula for how soft skills are gained and applied in the workplace.

How many times have you seen an advert that requires "a can-do attitude and teamwork"?

Yet soft skills are on all recruiters' must-have checklists.

In the case of Ph.D. applicants, these transferable skills are built thanks to all the healthy habits you've maintained through your education, including your:

• collaboration
• ability to meet deadlines

Soft skills hint to recruiters more about your character and style of work.

Here are some ideas as to which ones you can include within your resume:

37 PhD soft skills to spice up your resume:

• Critical / Logical Thinking
• Problem-Solving
• Time Management
• Brainstorming
• Creativity and Innovation
• Meeting Deadlines
• Working Under Pressure
• Negotiation
• Project Management
• Organization
• Prioritization
• Flexibility
• Independent Work
• Ethical Decision-Making
• Leadership or Mentorship
• Collaboration
• Teaching or Lecturing
• Conduct Meetings
• Supervision
• Feedback and Evaluation
• Motivating Others
• Communicating Ideas
• Presentation
• Constructive Debating
• Leading or Participating in Group Discussions
• Public Speaking
• Accelerated Learning
• Attention to Detail
• Writing Proficiency
• Quantitative Literacy
• Listening and Reflection

When describing your leadership or mentorship soft skills, here are a couple of questions you could answer within your resume to qualify your achievements:

• What actions did you take to maintain a constant and successful team dynamic?

Mix in extracurricular certificates

Back in the day, you earned a couple of extracurricular certificates and wondering if you should include those on your Ph.D. resume.

Again, it's a matter of analyzing how necessary your certification is for the job.

E.g. if AICPA's CPA certificate is listed as obligatory within the job description - and you have earned yours - you know what to do.

Certificates show that you're willing to put in the extra effort to stay relevant. Proving that you're committed, flexible, and a life-long learner.

So, think about the relevancy the certificate would have within your field.

Then, consider including some of these popular certificates:

Top 50 PhD certificates from various institutions for your resume:

• Association of Clinical Research Professionals (ACRP) - Certified Professional
• ACRP - Clinical Research Associate Certification
• ACRP - Clinical Research Coordinator Certification
• American Health Information Management Association - Coding Specialist Physician-Based Certification
• Nationally Registered Certified Patient Care Technician
• National Healthcare Association (NHA) - EKG Technician Certification
• NHA - Phlebotomy Technician Certification
• NHA - Clinical Medical Assistant Certification
• American Association of Medical Assistants - Medical Assistant Certification
• Red Cross - Nursing Assistant Certification
• Behavior Analyst Certification Board, Inc. - Registered Behavior Technician
• American Association of Professional Coders - Certified Professional Coder
• Pharmacy Technician Certification Board - Certified Pharmacy Technician
• Society for Clinical Data Management - Clinical Data Manager
• American Medical Writers Association - Medical Writer Certified
• Board of Editors in Life Science - Board-Certified Editor in Life Science
• International Society for Medical Publication Professionals - Certified Medical Publication Professional
• Regulatory Affairs Professional Society - Regulatory Affairs Certification
• Google Project Management Professional
• Society of ​​Petroleum Engineers - Petroleum Engineering Certification
• American Institute of Chemists - National Certification Commission in Chemistry and Chemical Engineering Certification
• Coursera - Software Engineering MasterTrack Certificate
• Cisco Certified Network Professional in Service Provider Operations
• CompTIA Security+ Certification
• (ISC)² Certified Information Systems Security Professional
• American Society for Quality (ASQ) - Quality Engineer Certification
• ASQ - Reliability Engineer Certification
• Advanced Certificate Program in CFD-Aircraft Aerodynamics
• Engineer in Training License and Certification
• Society of Broadcast Engineers - Certified Audio Engineer
• Association of Technology, Management, and Applied Engineering - Certified Technical Professional
• International Council on Systems Engineering - Systems Engineering Professional Certification
• American Academy of Project Management - Certified Planning Engineer
• Heating, Ventilation, and Air Conditioning Master Specialist Certificate
• iNARTE Electromagnetic Compatibility Designer Engineer Certificate
• American Academy of Environmental Engineers and Scientists - Board Certified Environmental Engineer
• American Public Power Association - Key Accounts Certificate Program
• Global Association of Quality Management - Certified Agile Developer
• Environmental Protection Agency - Operator Certification Program Management
• Institute of Management Accountants - Certified Management Accountant
• National Association of Certified Public Bookkeepers - Certified Bookkeeper
• Association of International Certified Professional Accountants - Certified Public Accountant
• Chartered Financial Analyst Institute - Chartered Financial Analyst
• Internal Revenue Service - Enrolled Agent
• The Institute of Internal Auditors - Certified Internal Auditor
• Association of Certified Fraud Examiners - Certified Fraud Examiner
• National Association of Sales Professionals - Certified Professional Sales Person
• Institutes of Management Consulting - Certified Management Consultant

PhD resume: let’s get creative with a few more resume sections

When completing your Ph.D. resume, you should always find ways to stand out from the crowd.

That’s why we’ve compiled for you some of the most popular sections which you could add to your resume.

Before doing so, always question each section's relevance to the job you're applying for.

• Publications or Projects - focus on topic, methodology, and impact; include your grant ID code, if your research won any funding
• Academic Awards - once more, consider if those would shine a better light on your expertise
• Conference Presentations - this would showcase your public speaking abilities
• Language Skills - be honest when listing your language proficiency

One final word of warning - your Ph.D. resume offers limited space to showcase your expertise, so try to make the most out of it.

key takeaways

• The extra effort to align your Ph.D. skills with the job you're applying for always gets recruiters' attention.
• Include as many relevant keywords within the header and summary of your Ph.D. resume.
• Have separate sections, detailing how your academic background has helped you attain experience, skills, and certifications.
• List chronologically all degrees you've earned through your education, with an adaptable approach to details.
• Remember that the recruiters or the ATS assessing your resume may not be that scientifically literate. Substitute complex terminology with impact and results.

Looking to build your own PhD resume?

• Resume Examples

Should You Add Unrelated Work Experience on Your Resume?

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Complete Data Scientist Resume Guide (with Templates)

Data science is a competitive field and creating a resume that helps you stand out can feel monumental.

Below, we break down the process of writing a data science resume from scratch.

Alternatively, use our data science resume templates from real candidates who landed the job with Exponent.

We'll help you:

• Write a resume that attracts more interviews, whether you're a junior or senior data scientist.
• Highlight your most relevant projects that align with the goals of the role you're applying for.

Key Takeaways

• A well-structured data science resume should have detailed hands-on data experience, highlighted projects and impacts, a technical skills section, and relevant education.
• Customizing your resume for each job application is essential. Tailor your summary and role descriptions to align with specific job requirements and company culture. Mapping your accomplishments to the Data Scientist core skills will increase readability and alignment for the majority of job descriptions in the market. 
• Balancing technical skills with soft skills, such as communication and critical thinking, boosts your resume’s effectiveness.

[Template] Senior Data Scientist (Pinterest)

This template comes from a senior data scientist who successfully landed a role at Pinterest.

You can copy this template on Google Docs and use it as a foundation for your own resume.

Why this resume works

• Quantified Achievements: This resume effectively quantifies accomplishments, such as improving MDG accuracy from 96% to 98.8% and driving a 15% revenue increase at Merkle.
• Relevant Technical Skills: The resume showcases the candidate’s proficiency in key tools like SQL, Python, and data analysis techniques.
• Project and Tool Integration: The resume integrates projects and tools directly into the work experience descriptions, showing practical, hands-on experience.

[Template] Junior Data Scientist (Apple)

This is a real resume example from a junior data scientist who landed a job at Apple after working as a data analyst and intern.

Copy this template on Google Docs to kickstart your resume.

• Quantified Achievements: The resume effectively quantifies contributions, such as improving model accuracy and driving a 15% revenue increase. Highlighting these metrics shows the direct impact of the candidate’s work.
• Project and Tool Integration: By integrating projects with work experience, the resume clearly demonstrates practical abilities in data analysis and model development.

Key Elements

Every successful resume should include:

• Contact Information: Place your name, location, email, and links to your LinkedIn or GitHub profiles at the top. Keep this section concise—no more than two single-spaced lines.
• Professional Summary: Provide a brief overview of your key achievements and skills. For junior candidates, focus on education and relevant projects. This summary should be 3-5 lines, covering who you are, what you do, your major accomplishment, and your next career goal.
• Work Experience: Highlight your most relevant roles in reverse-chronological order, focusing on measurable achievements.
• Projects: Showcase projects that demonstrate your data analysis and modeling skills. Align these projects with the industry and the products or services you’re targeting. As you gain more experience, this section may be streamlined.
• Skills Section: Highlight the technical skills and tools relevant to the job. List your most proficient skills first, as hiring managers often assume these are your strongest.
• Education: Include your degree(s) and any relevant coursework or certifications, especially if you’re a recent graduate. For experienced candidates, this section should be brief.

Formatting Tips

• Use Reverse-Chronological Order: Start with your most recent work experience.
• Keep It Concise: Aim to keep everything on a single page if you have less than five years of experience. For those with more experience, two pages are OK.
• Be ATS-Friendly: Make sure your resume can pass through Applicant Tracking Systems (ATS) by using simple formatting and relevant keywords. This will ensure it reaches a hiring manager.
• Professional Design: Use a clean, professional format with easy-to-read fonts like Arial or Calibri.

Professional Summary

Your data science resume summary should serve as your personal pitch, summarizing your background and experience in 3-5 sentences.

Structure it like this:

• Who you are
• What you do
• What you’re known for (significant career accomplishment)
• Where you’re going next (target role - function and/or industry)

For example:

Led, developed, and launched X product into a new market, resulting in X% market adoption rate and $XXX revenue.

Focus on your most notable skills and achievements, such as successful model deployments, A/B tests, or impactful statistical analyses.

Avoid discussing personal career ambitions in this section.

Here’s an example summary from a senior data scientist:

Senior Data Scientist with 10+ years of experience in X and Y industries. Developed a machine learning algorithm that achieved 94% prediction accuracy in global public health applications. Currently seeking to join X company as their next Lead Data Scientist.

And here’s an example from a new graduate:

Recent CS graduate from CMU & Data Scientist with 2 years of experience in Python. Developed pricing models for SaaS products as an intern at a global SaaS HR Services company. Led the university statistics club, growing it to 8 campuses in 6 months.

Work Experience

Focus on your achievements rather than merely listing job duties. Align your accomplishments with the core skills required for the data scientist role you’re targeting.

Using a “skill: accomplishment” framework will help your resume read like a job description, aligning your skills with the majority of job descriptions.

Showcase the direct impact you had on key performance indicators (KPIs) such as revenue, growth, or retention.

Here’s an example from a senior data scientist at a large video game company:

Financial Modeling & Data Accuracy: Identified and corrected a flaw in the financial model that overlooked up to 10 million accounts, leading to accurate data capture. Optimized model run time from 3 weeks to 2 days and developed an alternative hypothesis, enhancing overall model reliability.

Data Management & Compliance: Built an audit table that accurately stores financial data linked to the latest Geo-IP history, ensuring precise financial reporting and compliance.

Automation & Reporting: Created automated reports in Tableau, Excel, and FSG, streamlining processes for Tax and Accounting. Translated Hive SQL and Unix Shell Scripts into Python scripts in Databricks, automating workflows and improving efficiency.

Process Improvement & Efficiency: Developed Python scripts that reduced manual work by 500 hours annually, significantly increasing productivity and freeing up resources for higher-value tasks.

Data Analysis & Strategic Insight: Conducted ad-hoc analyses to identify year-over-year trends and explain sales behavior, providing actionable insights that informed strategic decision-making.

Here’s an example from a mid-level data scientist at AT&T:

• Propensity Modeling & Customer Targeting: Developed and managed propensity models, improving customer targeting accuracy by 22%, leading to a 15% increase in campaign conversion rates.
• Predictive Modeling & Churn Prediction: Implemented speech-to-text models that predicted churn with 85% accuracy across mobile and internet services.
• A/B Testing & Customer Engagement: Designed and executed A/B tests that personalized customer interactions, resulting in an uplift in customer engagement and an increase in promotional campaign success rates.
• Team Leadership & Project Efficiency: Guided team members and interns, ensuring accurate query logic and model development, which contributed to a 25% reduction in project turnaround time.

Past Projects

Highlight projects that demonstrate your technical skills and problem-solving abilities. Include details like the tools and languages used (e.g., Python, R, TensorFlow) and the outcomes of your work. As you gain more experience, you can streamline this section.

For example, link to a GitHub repository of an open-source project you contributed to.

Technical Skills

Tailor your skills list to the job description, focusing on the languages, frameworks, and workflows that are most relevant. It’s better to highlight fewer skills that you are truly proficient in than to overstate your abilities.

Common technical skills to include on a data science resume:

• Python Data Analysis Frameworks (NumPy, Pandas, Scikit-Learn, Keras)
• Data Visualization (Tableau, Excel)
• Machine Learning Techniques (supervised and unsupervised learning)

Productionizing Models

Additionally, map your technical skills to your work experience where applicable so the reader can see how you’ve applied these skills in real-world scenarios. This also improves searchability on LinkedIn and in applicant tracking systems.

Propensity Modeling & Customer Targeting: Leveraged Python Data Analysis Frameworks (NumPy, Pandas, Scikit-Learn) to develop and manage propensity models, improving customer targeting accuracy by 22%. Applied supervised learning techniques to enhance model precision, leading to a 15% increase in campaign conversion rates.

Predictive Modeling & Churn Prediction: Implemented speech-to-text models using Python and integrated Scikit-Learn for predictive analysis, achieving 85% accuracy in churn prediction across mobile and internet services. Productionized these models, ensuring they were seamlessly deployed into the business pipeline.

A/B Testing & Customer Engagement: Designed and executed A/B tests using SQL and MySQL to analyze customer data and personalize interactions. This approach, combined with machine learning techniques for customer segmentation, resulted in an uplift in customer engagement and improved promotional campaign success rates. Utilized Tableau and Excel for clear and effective data visualization, facilitating decision-making.

Team Leadership & Project Efficiency: Guided team members and interns in using SQL and Python to ensure accurate query logic and model development. Mentored the team in best practices for deploying machine learning models , contributing to a 25% reduction in project turnaround time.

If you’re still building your experience, focus on the proactive steps you’ve taken to develop your skills—such as bootcamps, courses, or mentorship programs.

Soft Skills

Soft skills are just as important as technical skills in data science. You’ll be expected to work cross-functionally and clearly explain your findings to product managers, engineers, and business leaders. Articulating how you collaborate with others can set you apart, especially for mid-career to senior-career level roles.

Highlight key soft skills in your work experience:

• Communication: Ability to convey complex information clearly to non-technical stakeholders.
• Critical Thinking: Ability to analyze data objectively and challenge assumptions.
• Creativity: Ability to approach problems with unique solutions, often filling in the blanks of missing data.

Here’s an example:

Stakeholder Communication: Primary point of contact for the senior leadership team; presented performance results, development milestones, and new use cases throughout the development cycle.

Education and Certifications

Education and certifications are particularly important in data science. List your degree(s), relevant coursework, and certifications in data science or machine learning.

For recent graduates, you can include additional details like relevant projects or internships. For those transitioning from other fields, consider condensing the education segment to emphasize the qualifications most applicable to data science.

Resume Customization

Customizing your resume for each job application is essential. By tailoring your resume to reflect the nuances of the position and the company’s culture, you demonstrate that you’ve thoroughly considered how well-suited you are for the role.

Rework your resume summary to align your skills, experience, and qualifications with the job you’re applying for. Mapping key skills, technologies, and qualifications across your summary, work experience, and skills section will help create a cohesive narrative that highlights your value to the organization.

Here’s how to structure your summary in 3-5 lines:

• Example: Led, developed, and launched X product into a new target market, resulting in X% market adoption rate and $XXX revenue.

Data Scientist with a strong track record of leveraging advanced statistical techniques and machine learning to drive strategic decision-making and business outcomes at scale. At Pinterest, I spearheaded data initiatives that enhanced product marketing, reducing user churn by 21%. I am now seeking to apply my expertise in a forward-thinking organization, aiming to lead data-driven innovations in the tech industry.

Customize your past employment descriptions by emphasizing projects or tasks directly relevant to the role you’re targeting. For example, if expertise with ETL tools is required, prominently feature any experience you have with developing ETL pipelines. Use the Task-Action-Result framework to build out your resume bullets.

Additional Sections

Enhance your resume by including sections like:

• Publications: Highlight your research contributions.
• Presentations: Show your ability to communicate complex topics.
• Awards: Demonstrate industry recognition.
• Professional Affiliations: Indicate active involvement in the data science community.

Including these sections can help demonstrate your expertise and commitment to the field.

Don’t overlook a section on blog posts. These entries can reflect your active interest and ongoing dialogue about current trends or discussions relevant to the world of data science. Optimize these segments based on their alignment with the target job requirements and your vocational experiences.

How long should a data science resume be?

The length of your resume depends on your work experience. Typically, a data science resume should be one page for up to 5 years of experience. If you have 10+ years of experience, you can expand your resume to two pages. Even experienced professionals should focus on the most relevant details.

What are the key elements of a data science resume?

A data science resume should prominently feature your contact information, a summary, relevant work experience, projects, key skills, and educational background.

How should I format my data science resume?

Maintain a clean and professional design. A single column is preferred. Use easy-to-read fonts, incorporate sufficient white space for clarity, and use bullet points for better legibility.

Why is it important to customize my resume for each application?

Tailoring your resume to each job shows that you’ve focused on the role’s nuances and demonstrates your commitment, increasing your chances of capturing the hiring manager’s attention.

What additional sections can enhance my data science resume?

Enhance your resume with sections for publications, presentations, awards, professional affiliations, and blog posts. These additions can showcase your research, communication skills, and active participation in the industry, reinforcing your expertise and commitment to the field.

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How to Write a Data Science Resume for University Graduates in 2024

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How to write a data science resume for university graduates?

Getting your first job after university is not straightforward in any discipline.

Data science is no exception, but easy is no fun, right?

It took everything you had to make it through your education and now you’ve got to start again with the job market. You feel like a newbie. And how does a newbie make an impact? How does a fresh face stand a chance against veteran data scientists with years of experience?

Like I said, it’s difficult.

But data science is a fantastic field to be part of - companies are hiring all over the place . They are looking for enthusiastic and driven new recruits! You know you’d make a superb data scientist and you must show them that.

How? With an awesome resume!

And we’re here to help!

Let's go through how to write a data science resume for university graduates and share tips with you to help you represent your wonderful self on a sheet of A4. (And then, you can jump straight to our ultimate career guide to discover 10 of the best data science job boards you should bookmark before you send out your resume .)

A couple of general tips before you start

Let’s start with some things to consider before you take pen to paper… or fingers to keys.

How to organize the writing process?

Before you start writing anything, you need to decide on your audience (the employer) the purpose of your text (to show how awesome you are) and the goal you want to achieve (an invitation to interview ). Write it on a post-it note so you can always remind yourself. When you start writing, write everything. With a CV this will be all your experience, all your skills, everything you want to say about you, your ‘master-copy’ if you will.

After that, is the editing, cross-checking what you have written with your little post-it note, taking all the relevant information and putting it in a neat little package.

Why are keywords important?

Recruiters spend anywhere between 5 and 30 seconds looking at a CV. And they don’t read it, they scan it looking for keywords. Words that signify an applicant is even worth considering.

Many even use an Applicant Tracking System (ATS) which is computer software that does the scanning for them.

Skills are the main keywords , but we will get into that further on.

The company name is a great keyword. It will grab attention and it’s a small touch that makes an enormous difference.

You’ll find the best keywords in the job description and a clever idea is to have a quick google of trending keywords. The more you have in your data science resume , the better. And scattering them through your CV will make the recruiter (or her robot) scan the entire CV.

Lying in your CV is tempting but, oh, so stupid. So, if you want to boost the number of keywords with true backable skills, take some extra courses. Here at 365 we have recognized and valuable courses that will make your CV shine with professionalism. Click here to see some.

Now on to the good stuff!

How to write your data science resume?

When you are transforming your ‘master-copy’ into your ‘masterpiece’ it’s good to keep it on one page. Some recruiters are fine with having more but from what I’ve found, they are the minority. The ones who do like the CV on one page, REALLY like it on one page. Double-side it if you really need to but if you focus on your audience and your goal, one page should be fine.

Have a play around with font and sizing, but I’d recommend something easy to read and a size that fits your CV nicely. Don’t go any smaller than 10 though, find a way to lose some words instead.

Choose a photo... then get someone else to approve it. Get one taken professionally if necessary. Photos on a resume are common now* so do it right. And smile, it’s not a passport!

* Some companies explicitly state that photos are not allowed in a CV due to their equal opportunity policy. Those are usually large corporations or banks, so be careful with that.

Contact information

This should be the easy bit - make sure you spell your name right, and obviously, you’re not going to get any call-backs if you write the wrong telephone number.

Feel free to add any social media accounts to this section, LinkedIn, Facebook etc. If you have a Blog or a website which shows off your skills.

Have a read of our personal branding piece for invaluable information to make your online persona as suave and sophisticated as your real-life one.

Trust me, you may think your privacy settings are water tight, but the internet is sneaky and a shirtless photo of you riding a rodeo bull can always slip through the net.

Sort yourself out a nice profile photo and tactically share some professional sounding posts from BBC news for all to see, you can always change it back once you get hired!

Objective vs summary (or not)

First, What’s the difference between an objective and a summary?

An objective is a short statement saying what you want career-wise.

For example, my objective would be something like:

“English linguistics graduate looking for a writing job, with high salary to fund my cat clothing obsession” If you ask me, your CV itself is an objective. You want a job for the company you are applying for. So, you gave them your CV.

Unless you are planning on telling them what they want to hear, don’t bother with an objective.

How about a summary?

The goal of a summary, and in fact your whole CV is to show your best qualities in short space.

So, remember to keep it focused – don’t waffle on about irrelevant points. Keep your audience in mind.

Avoid fluff words - “enthusiastic, team-player, loves a challenge, quick learner”, everybody says this, prove your statements with measurable facts.

Sell yourself, don’t be shy, you’re great! You want to stand out in a crowd of experienced data scientists. Put yourself in an employer’s shoes, what would you want in a candidate? Someone just like you right? Show them that.

If you really, really need to save space, you can put your summary in a personalized cover letter. A cover letter gives you more words to play with, a chance to thrown in some specific keywords and most positions require a cover letter.

The skills section is one of the most important, especially for a data scientist and especially especially for a recent graduate! Because you haven’t got much experience, skills are your strength. Skills are your keywords , and you should litter them throughout your data science resume for university graduates. The skills list is just for reference, the examples of your skills are where you show off.

I was going to say go and do your research, find trending buzzwords, work out what skills a data scientist should have etc. Instead, I found a site that lists almost all the skills that employers will be looking for in a data science application.

You’re welcome!

Your job is to go through these, pick out the skills you own and think of times when you have used them. Write them all down. These will lead to that interview.

There are two types of skills, hard and soft: hard are your data science technical skills – R, Python , SQL etc.

Write down as many real-world examples using these skills as you can.

Stating how you “ used Tableau to visualize the monthly sales at the car dealership you were interning at. This led your manager to see when the company sold the least add-ons and in turn promoted them at this time, increasing sales by 50% over 3 months"  is more hard-hitting than just writing   “ Tableau ” in your ‘Skills’ section.

Note that hard skills can be very job specific. So be sure to check job descriptions; one employer may prefer R over Python and you need to adjust accordingly.

Soft skills are your transferable skills – time management, leadership, communication, etc.

Having examples of these is arguably more important than the hard skills, particularly for a graduate. They will let employers know that you can adapt to and thrive in the work-place. Unless it’s a research-based position, most employers will want to know that you are a capable data scientist outside of academia.

So, you should end up with a bunch of skills and a handful of good examples of when you’ve applied these skills. This will be your master list. You should make it as exhaustive as you can because it will be the backbone for the rest of your data science resume for university graduates.

I highly suggest putting your education right after your summary (if you choose to have one). As a graduate, it is your strongest area. You can fill this section with skill, experiences, and work attitudes while showing off your wealth of knowledge at the same time.

Start with your most recent qualification and work backwards. Don’t bother with high school grades-waste of space. If you were accepted into university they couldn’t have been that bad, and they’re certainly not relevant. Lay it out with your degree > school > dates unless your degree is not directly related to data science and your university is one of the best, then you can go with school > degree > dates.

You need to write as many things as you can think of (keep them in your master-copy). If it shows off any skill, write it down. But again, be specific.

This is another chance to brag a little, talk about your thesis, publications, research projects. A benefit of being an academic is your breadth of knowledge. So, show this off.

Jobs in the data science field vary and some positions require more time in academia than others. Your theoretical knowledge is your desirable trait at this point, but lack of experience can raise warning flags for certain employers. So, use this section to your advantage, fill it with technical skills and what you have learned and researched, but use the job description to judge how heavy you rely on it as you may want to put more effort into the next section…

I'm kidding, you’ve got loads of experience! You’re a young adult who has spent years at university - living life, learning new things, and growing into an awesome data scientist. Now is just the point where you must find experience which is relevant and will make an employer want to get you on their team.

So, how do we do this?

Yes, again, we start strong! Don’t start with your most recent job if it was working as a waiter in a cocktail bar. Your 6-month internship with *insert big-deal company here* should be at the top. You can use your part-time student jobs though as they will highlight your transferable skills. The ones which flaunt that you can fit right into the workplace. Your killer Mai Thai may go down a treat at the staff party but no need to include it in your data science resume for university graduates. However, this is the best place to show off your ‘soft skills’ “Used my verbal communication skills to reduce customer compensations by 35% in June/July” for example.

Employers respect someone who works hard, especially through their studies.

Oh, you didn’t work during your studies? Bank of mum and dad kept you in the black?

No problem, then list your independent projects. Experience doesn’t have to be paid work. Have you been on Kaggle or GitHub? If not, get on there... like NOW ! They are great platforms to get some practical experience in data science. Kaggle gives you competitions to do and GitHub is a site for posting your code . These are also great points that you can talk about in your interview.

And speaking of things to bring up in the interview…

Activities and interests

This is where you put a little bit of personality, show them how interesting you are as a person on top of everything.

Don’t play it safe with “I enjoy reading, walking my dog and staying on late at work” Yawn! That’s why they call it 'interests'. Give the recruiter one last punch in the memory!

Do you play a weird instrument? Bungee jump? Collect dinosaur bones?

The more unusual the better!

You’ve already blown them away with your data science resume for university graduates. This will leave them with one last thing to make sure it sticks in their head.

Don’t put your references. I wouldn’t want my details handed around all over the place. When you’ve been offered the job, then give your references. Tell them they will be contacted. It won’t look good on you when your course lecturer responds with “who?” when asked to give a gleaming recommendation about you.

Excellent, I hope this has helped get you pumped to write an epic data science resume for university graduates. You’re going to do great, it’ll take some work on your part, sure, but follow these tips (and if you’re sensible, tips from other articles as well) and you’ll be well on your way to getting that data science job . And if you need more preparation, check out our course on Starting a Career in Data Science: Project Portfolio, Resume, and Interview Process .

And again. Good luck!  

World-Class

Data Science

Learn with instructors from:

Alan Mitchell

Alan is an SEO copywriter with experience in various types of digital content, such as well-researched career articles and interviews with renowned experts in the data science field.

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5 PhD Resume Examples Made to Work for 2024 

• PhD Student Resumes by Type
• PhD Student Resumes by Role
• Write Your PhD Resume
• Entry-Level
• Senior-Level

With the role of a PhD student, you’re the intellectual powerhouse driving groundbreaking research and contributing to the advancement of knowledge. Your expertise allows you to dive deep into complex subjects, developing innovative solutions and pushing the boundaries of what’s already known. 

At the same time, you’ll need to balance your responsibilities as a teacher as well, imparting your knowledge to the undergraduates at your institution. Crafting a resume and creating a cover letter that demonstrate your ability to shine in this diverse role is no easy task.

Luckily, we’re here to guide you through the maze of showcasing your academic journey. With our varied PhD application resume examples , you’ll find the ideal resume template to help you craft your own winning resume in no time.

or download as PDF

Why this resume works

• Thankfully, Liam does a great job highlighting his internship and project achievements and how both are applicable in helping sustainably improve production.

PhD Student Resume

• One strategy you can use to boost the chances of your PhD student resume to clinch an enrolment slot into the health sciences department is by harnessing the achievements from a volunteer project that brought screening services to a marginalized community.

PhD Application Resume

• Therefore, achievements in increasing student participation and engagement accompanied by metrics in improved grades and assessment scores would go a long way to prop your suitability.

Engineering PhD Resume

• Hence, including it in your engineering PhD resume will do wonders and show that you’re a state-approved individual who knows what they’re doing. Employers will almost always prefer a licensed professional over an unlicensed one.

PhD Scientist Resume

• That’s where you’re going to include some numbers to add readability to your PhD scientist resume. Now, don’t be random and use metrics for impactful bullet points like decreasing experimental errors or analyzing a large number of samples.

Related resume examples

• Research Assistant

Adapt Your PhD Resume to the School You’re Applying to

As an aspiring PhD student, your resume is your scholarly calling card, showcasing the intellectual artillery you bring to the academic battlefield. 

This is where you can showcase your skills and express why you’re the right person to join the program. To do this, tailor your skills section to align with the specific requirements of your desired program and department. 

Highlight your expertise in research methodologies, statistical analysis, and any specialized software or equipment you’ve mastered. For the more technically inclined, don’t shy away from showcasing hard skills like Python, Matlab, Java, or Tableau.

You can also include a couple of soft skills because they’re essential for giving lectures and mentoring students. Just remember to reinforce them with demonstrable examples in the experience section later. 

Want some pointers?

15 top PhD skills

• Microsoft Office
• Google Sheets
• Research Methodologies 
• Academic Writing
• Public Speaking
• Lesson Planning
• Grant Proposals
• Grading Essays
• Collaborative Research

Your PhD work experience bullet points

From conducting groundbreaking research to publishing papers, your journey as an academic is about more than just the day-to-day grind. In the experience section of your resume, the real spotlight should be on your transformative contributions. 

If this isn’t your first PhD, highlight achievements from your previous studies like securing research grants, publishing impactful papers, or successfully leading research projects. If you’re moving up from postgraduate studies, highlight the best and most impressive accomplishments from your master’s and bachelor’s degrees. 

Add some numbers to make your accomplishments pop. This could be the number of students mentored, successful experiments and research projects, or the reach of your published work. 

• Highlight the number of your articles or research papers that were published in reputable journals.
• Quantify the success of research projects with metrics such as project scope, budget management, or studies carried out.
• Demonstrate your ability to secure research funding by specifying the number and value of grants obtained.
• Showcase your impact as a mentor or tutor by talking about the number of students you’ve guided or the way they were able to improve their grades through your help.

See what we mean?

• Created engaging course materials using Articulate Storyline, resulting in a 39% increase in student engagement and comprehension
• Managed EHR software to document patient assessments, vital signs, and medication administration, maintaining 99.99% accuracy in recordkeeping
• Partnered with farmers to develop customized crop management plans, resulting in a 31% increase in yields
• Assisted in the development and implementation of assessments, leading to a 33% reduction in student dropout rates

9 active verbs to start your PhD work experience bullet points

• Innovated 
• Collaborated

3 Tips for Writing a PhD Resume With Little Prior Experience

• Mention your involvement in conferences and workshops. This will showcase your ability to engage with and contribute to the broader scholarly community.
• Echo your passion for knowledge throughout your resume, and look into the future. Outline your career objectives , illustrating your commitment to making a lasting impact through your PhD studies.
• Any and all academic achievements look great on a PhD resume, so make sure to add them. Talk about your GPA, awards won, or competitions you’ve participated in to show your drive as a college student.

3 Tips for Writing a PhD Resume for Your Second PhD

• Research is often independent, and academics are sometimes considered lone wolves. That’s why it’s important to emphasize your leadership and collaboration skills explicitly. The school needs to know you can mentor students and collaborate with other colleagues effectively—so turn up the enthusiasm for this area!
• If you’re going for a research role, it’s essential to be at the forefront of your field—following all the latest papers and studies. You can show this by mentioning your participation in research initiatives or the conferences you like to attend. 
• Show that you put just as much effort into your students as you put into your research by sharing student performance and engagement metrics. You can also discuss your favorite lecture and seminar-planning techniques to convey your passion and commitment. 

Absolutely! While not mandatory, a tailored career summary can be a powerful tool. Customize it for the PhD position, mentioning the specific program and academic role. Don’t forget to highlight things like research methodologies, data analysis, and any unique contributions to your academic field.

Choose a clean and professional format that prioritizes your academic achievements and research experience. Use clear headings, bullet points, and a consistent structure, much like you would in a research paper. 

Include skills that align with the specific requirements of the PhD program and your academic discipline. Highlight technical and soft skills relevant to research, teaching, fieldwork, and collaboration.

Data Scientist Resume Sample (PDF): Full Guide and Example

Introduction, data scientist resume format for freshers and experienced, 1. personal information, 4. education, 6. work experience, 7. awards and certifications, 8. interests and hobbies, data scientist resume sample, additional tips, 1. how do you list data science skills on a resume, 2. what are the skills of a data scientist, 3. what should a data scientist’s resume look like, 4. how can i improve my data science cv.

Data science is currently one of the most in-demand careers and will continue to remain so in the future. If you’re looking for a job in data science, you’ll need a strong data scientist resume and cover letter to stand out from the crowd.

Data is present in almost every industry imaginable, which is one of the reasons why businesses are becoming interested in data science. Another motivation of data science is the notion that data will continue to be an important part of our lives indefinitely. That said, it’s critical to keep up with the latest data science trends, as they may be beneficial to your company’s growth. The top ten data science trends for this decade are shown below:

• Big Data on the Cloud
• Augmented Analytics usage
• Data Cleaning Automation
• Natural Language Processing
• Quantum Computing for Faster Analysis
• Democratizing AI and Data Science
• Automation of Machine Learning (AutoML)
• Computer Vision
• Generative AI
• Blockchain in Data Science

Companies need a data scientist to do these jobs because data science pulls together subject expertise from programming, maths, and statistics to produce insights and make sense of data. Once they’ve figured out how to make sense of the data, they communicate it to the information technology leadership teams and use visualizations to comprehend the patterns and trends. Data scientists also use machine learning and artificial intelligence, as well as their programming skills in Java, Python, SQL, Big Data Hadoop, and data mining. They must have excellent communication skills to properly communicate their data-finding ideas to the organization.

Confused about your next job?

They also help to reduce risk. Data scientists have been taught to look for data that is unusual in some way. They use statistical, network, path, and big data approaches to forecast danger and use them to produce alerts that assure prompt responses when odd data is detected.

They assist in the delivery of pertinent products. One of the benefits of data science is that it allows businesses to determine when and where their items sell the best. This can aid in the delivery of the correct items at the right time—as well as the development of new products to fulfill the needs of customers.

In this blog, you will learn about:

• A sample data scientist resumes better than most.
• How to ace your data scientist job description on a resume.
• How to write a resume for data science jobs that gets the interview.
• Why picking the right few data scientist qualifications is the #1 key to getting hired.

A typical resume is divided into various parts. In this section, you will come across the best practices to write a job-winning resume.

Before diving deeply into each section of the resume, let’s look into some common useful tips:

• Make section heads bold and larger.
• Use a classy typeface for your resume.
• Use proper white space.
• Create a resume that is as long as you need it to be. It is acceptable to have a resume that is longer than one page. It’s not a good idea to skip over crucial aspects of your profession.
• Set the margins to a single inch on both sides.
• Use reverse-chronology order for your experiences.
• Consider using a single or 1.15 line spacing.
• Choose a relevant template that showcases the information in a clear manner.

It is a crucial section concerning your contact information and your activities on social media sites like Linkedin, Stackoverflow, Medium, etc. You cannot afford to make mistakes here. The interviewer comes across several resumes with incorrect email addresses or phone numbers which it becomes difficult for them to convey the result to their candidates. So there is always a need to cross-check this section before submitting your resume. The mandatory fields include:

Full Name Title – “Data Scientist” Phone Number Email Address Location (Optional)

On the other hand, if you are active on GitHub for your data science projects or your contribution to an open-source project, then include the link to your profile. By doing so, you can showcase your skills which will help to stand out your application and increase your selection chances for further rounds. The same point is valid for other platforms like Medium, Linkedin, etc.

So it’s less important to be creative here than accurate here.

This section is placed on the top of the resume. It is the short description that summarizes your whole resume and at this point recruiters and hiring, managers get the idea of whether to proceed further or to reject the candidate. You have to summarize your years of experience, skills, and education in an easy-to-read format. This part shouldn’t exceed 4-5 lines. Firstly, tell about the years of experience you have as a data scientist and what was your role in previous companies (if experienced), secondly, mention your primary technical skills, and lastly, you can tell about your important certifications relevant as a data scientist role. The format may vary.

By going through all the information here, recruiters decide whether you are a good fit for their company or not. The main aim is to impress the hiring team so that they get forced to look at the best part of your resume and know more about you. This section needs to be short and creative because there are hundreds of resumes that recruiters come across in a single day, so they don’t have time to study the whole resume of each candidate. They spend a few seconds on each resume, so you need to be serious about this part as it is the deciding factor for your job.

In the above example, the incorrect section sounds easy to go to, not much relevant information is provided by the candidate that can influence the recruiter to shortlist it. But the correct section is way better, it is emphasizing more on candidates’ skills and achievements that can make recruiters shortlist them.

This section conveys your technical and soft skills. The point here is that you need to be specific about the role you are applying for (here Data scientist). Your skills should revolve around data science, machine learning, NLP, artificial intelligence, and related domains.

Mention the skills that convince your recruiters that you bear most of the required technical skills and software knowledge for the applied role. This is the time to showcase your knowledge related to programming languages, data science frameworks, libraries, tools, etc. Try to keep the technical skills and soft skills separate so that they can be distinguished. It enables an easy understanding of your skills to your recruiters.

Do not exceed the count of your skills, be specific. Mention only those which are most important for the data scientist role. Most importantly, you should be confident about your skills, for example, add only those languages in which you are comfortable coding because the interviewer might ask you to code for a problem statement just to test your coding skills in the mentioned programming language. Do not brag about your skills here. Just mention your expertise areas and if you have beginner knowledge of any skill and you want to add it to the bucket list as it’s crucial for the data scientist role, you may write (beginner).

In this part of the resume, you have to include your education in reverse chronological order. This section may vary for freshers and experienced individuals. If you are a fresher, you can include the following information:

• College degrees (Degree name, college name, GPA score)
• Intermediate school (School name, percentage/GPA score)
• High school (School name, percentage/GPA score)

If you are an experienced individual:

You may skip mentioning your schooling and write only about your bachelor’s and master’s degrees. The main focus will be on the Work experience part.

In the above example, you can see the correct way is to properly address your education details to the hiring team so that they can have a clear knowledge of whether the candidate has done relevant courses as a part of their education.

At this part of your resume, you may expect various kinds of questions from your interviewer which will describe your technical skills and soft skills. You have to include your data science-related projects that describe your capability to work on company-level projects. Include the following details about your every project:

• Your responsibility
• Technologies used
• State the quantitative result of the project.

If you’re just starting in your data science career and don’t have many options, including academic tasks you had to complete in this field or related fields.

Note: It’s a good practice to update information about your projects in your Github profile. Don’t brag about your roles and responsibilities because interviewers may cross-question for the same so as to check your skills like leadership team management, time management, etc.

This section is more important for experienced individuals. The basic structure of adding your work experience is as follows:

• Position name
• Company Name
• Responsibilities & Achievement
• Technologies you worked on

Your most recent employment should be listed first, followed by the job before that, and so on in chronological sequence.

A few factors influence how far back you can go in terms of experience. You usually don’t want to go back more than five years.

Keep in mind that, while you don’t have to provide everything about your experience, you should make sure that whatever you do include appears to be seamless. For recruiters and hiring managers, gaps in your work experience section of more than six months are a huge red signal. If you have a gap like this, you should surely explain it on your resume.

Make sure your job description focuses more on accomplishments rather than responsibilities. Employers want to see what you did, not simply what you said you would do.

Now you have a good chance to stand out from your resume from other applications. In this section, you can add your achievements that showcase your skills in the field of data science. If you don’t have one, you can add any accomplishments related to programming competitions and technical exams that you have appeared for. This part should have something unique. It will clearly show how much you could be a beneficial resource for the company.

Secondly, you can include your machine learning and data science-related certifications that helped in boosting your skills. This will build confidence in choosing you for the applied role.

Why would a recruiter need to know about your hobbies and interests, and what does it mean? You might think.

Your hobbies, on the other hand, disclose more about who you are as a person. If you have the space, include a hobbies section on your resume as a simple approach to add personality.

Consider what you’re attempting to communicate to potential employers with your hobbies and interests before putting them on your resume. Employers can get a sense of how you spend your time and what additional skills you have by listing your hobbies.

On the other hand, interests may suggest areas you’re actively researching or want to research, which could make you a strong fit for the organization.

Following are some of the additional tips that would be helpful in preparing your resume that may be useful in providing you with your dream job:

• Resumes should be customized for each position and organisation to which you are applying. To learn more about the company’s work, mission, and beliefs, visit their website and follow them on social media. Then think about how you may contribute as a Data Scientist.
• It’s an art in itself to communicate succinctly why you’re the best candidate for a job.
• Choose straightforward action verbs (resolved, trained, upgraded, improved, designed, directed, established, etc) that highlight your achievements and describe how you contributed to a team or project.
• Project work is very beneficial if you don’t have a lot of experience. As a result, put more emphasis on data science initiatives and learnings.
• Make a thorough spell and grammatical check. Demonstrate to employers that you are meticulous and detail-oriented. A second set of eyes might be beneficial, so have a friend or peer look through your resume.

You’ve given yourself the best possible chance of landing that data scientist job if you followed all of the preceding suggestions.

Let’s summarise everything we’ve learned so far:

• Prioritize the reverse-chronological format for your data scientist resume, and then follow the content style criteria.
• To get the recruiter’s attention, begin your resume with a summary or objective.
• Pay more attention to your accomplishments than to your responsibilities.
• Don’t stray from the path; be specific and give your relevant information for data scientist jobs.
• Create a compelling résumé for a winning application.

Ans. Include technical talents that are applicable to data science, with your strongest skills stated first. Examine the job description and see if your abilities match those needed for the role. To keep your talents section tidy, easy to read, and attract attention to significant skills, use bullet points.

Ans. Following are some of the technical skills required as a data scientist.

• Data analysis
• Data wrangling
• Data modelling
• Data visualization
• Programming
• Quantitative analysis
• Machine learning

Following are some of the tools that can be added to a data scientist resume:

Ans. A data scientist’s resume should be well-organized that convey all the required information to the recruiter. It should be data science-related fields specific, otherwise, there is a chance of rejection.

Following is a typical structure that should be followed while creating a resume:

• Contact information
• Profile/Summary/Objective
• Additional Sections (Awards, Certifications, Hobbies/Interests)

Ans. To begin, select a nice design for your CV. An excellent CV should reassure the recruiter about your problem-solving abilities and how you will help the company achieve growth and return on investment. Your CV should detail your experience as a data scientist and the various projects you worked on during that time.

• Data Science
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PhD resume examples & templates

Resume header

Awards, publications, or referees on your phd resume.

If you’re ready to apply for a PhD, chances are you’ll be asked to submit a resume so that the admissions committee can easily review your qualifications and interests. While a PhD resume will have many similarities with resumes created for job opportunities, there are a few key differences you’ll want to keep in mind while writing.

Luckily, Resume.io is here to help. With 350+ resume examples and writing guides, we’re an expert resource for job seekers (and students!) in all professions and stages of their careers. This PhD resume example and writing guide is designed to help you highlight your best qualities and get accepted into the program of your dreams. Here’s what we’ll cover:

What is a resume for a PhD program?

• How to write a PhD resume (tips and tricks)
• The best format for a PhD resume
• Advice on each section of your resume (summary, work history, education, skills)
• Professional resume layout and design hints.

In order to pursue a doctorate degree, you’ll need to apply to a PhD program, which generally lasts about 4-6 years. In order to evaluate your readiness to take on this challenging educational pursuit, most institutions will ask you to compile a resume (sometimes called a curriculum vitae in academia) showcasing your previous education, relevant work experience, academic interests, awards, and publications. You can think of a resume for a PhD program as a snapshot of who you are and the work or degrees you are most proud of.

PhD stands for “Doctor of Philosophy” and is the highest postgraduate degree available. To obtain a PhD, you not only need to be an expert in your field, but you must also present original and compelling research on a related topic, most often in the form of a dissertation, which is a written work that compiles your research and presents your insights into the chosen subject matter. A dissertation is then defended in front of a committee that decides whether or not you’ve met the standards to obtain your PhD.

How to write a PhD resume

The very first step in writing your PhD resume is understanding what sections to include. Your CV should contain the following elements:

• The resume header
• The resume summary (aka profile or personal statement)
• The employment history section for work or teaching experience
• The resume skills section which may include research interests
• The education section or academic history
• A publications or awards section
• A referees section if requested

While many of these sections can be found in some form on a standard resume for employment, there are key changes that academic committees will expect to see for candidates pursuing PhDs. 

Once you’ve identified the PhD program you’d like to apply to, it’s important to research the application process and any particular focus areas of the program. This will allow you to tailor your resume to contain the information most important to the selection committee. Be sure to include only the most relevant examples of work experience, while leaving out any odd jobs that are not related to the area you plan to study. For example, if applying to a PhD program in education, include your role as a graduate assistant for a university undergraduate course while leaving off a summer spent waiting tables at a local restaurant.

Choosing the best resume format for a PhD resume

PhD resumes are unique documents that will often deviate from other standard resume formats. Although the best format most closely resembles the reverse chronological structure of professional resumes, you’ll still need to make adjustments to best highlight your educational experience and research interests.

Make sure to order your resume with the most relevant sections first. While experienced job seekers may opt to place their education section at the bottom of their resume, PhD applicants should keep their education higher up on the page, usually after the summary section. PhD resumes should also contain an awards or publication section that are often rolled into the education section on other types of resumes. See our PhD resume example for more ideas on creating the perfect format.

The resume header is the attractive bar at the top or on the side of the page that contains your name, contact information, and any relevant social media profiles like LinkedIn. Your resume header serves a vital purpose in helping the reviewer to identify your document and making it easy to contact you about the next steps in the application process. The header also gives an attractive touch to your resume, but be sure to use a neutral color scheme and a professional font style as academic roles often call for a formal tone.

Resume summary example

The resume summary is the first section at the top of your resume that captures your interests, intentions, and key qualifications. For PhD applicants, make sure to include the name of the program and the university you are applying to (and don’t forget to update this for each different program – addressing your application to the wrong university is a serious mistake!) 

The summary should be about 3-5 sentences in length and should have a formal tone. Since PhD applicants often need to describe their research interests, the summary gives you the opportunity to do so without needing to use precious resume space to create a separate section for this information. Don’t forget to mention any relevant work or educational experience here as well as the goal of the summary is to encourage the reader to continue examining your resume. See our adaptable summary resume example below for more inspiration.

Get even more insight into the summary by checking out our related education resume examples:

• Early Childhood Educator resume sample
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Strong background in psychology- and therapy-based academic research environments requiring high levels of focus and attention to detail. Strong analytical and critical thinking qualities.

Employment history sample

The employment history section for a PhD will look slightly different than for those applying to traditional jobs as it will likely contain details of your research experience along with any educational work experiences you’ve completed. Make sure to organize the information in a logical manner, starting with the most recent position and working backward through all relevant roles.

Don’t forget to include the job title or name of the research role, the employer or institution name, the dates completed, and the location. Just as you would for a job, create 4-5 bullet points that explain your most relevant accomplishments and duties completed. See our adaptable employment history resume sample below.

Student Researcher, University of Washington, Seattle November 2021 - Present

• Gather, analyze and report data for academic research projects.
• Maintain donor databases and publications records.
• Assess research efficiency and identify areas for improvement.

Assistant Teacher, St. Clare School for Children with Special Needs , Singapore July 2018 - June 2020

• Aided special education teacher in collaborating with students with varying levels of physical and mental disability.
• Assisted teacher in preparing daily activities, lesson plans, and individual education plan (IEP) for each student.

CV skills example

While skills are seemingly more relevant for employment than for an academic position, the CV skills section can still be adjusted to meet the needs of a PhD applicant. The best part about the CV skills section is that it takes the form of a bullet point list meaning the reader is likely to take notice of this section early on while evaluating your resume. Use the skills section to highlight research areas, academic areas of interest, specialized skills from relevant roles, and any teaching skills you may have to put to use during the PhD program. Make sure to check the program requirements to see if there are any specific qualifications that can be easily added to this section. See our adaptable resume example below.

• Data Analysis
• Academic Writing
• Psychological / Behavioral Sciences
• Research & Analysis
• Therapeutic Crisis Intervention
• Mental Health Assessments
• Clinical Care
• Research-based Treatment
• Behavioral Health
• Marriage Family Therapy

PhD resume education section

The education section of your PhD is one of the most important areas that the selection committee will take into account. Make sure to place it high up on your resume, usually after the summary. The education section is the place to list all previous degrees, the awarding institution, dates attended, and location. Since you are applying for another degree program, it’s worth creating bullet points under your previous educational experiences to offer insight into your most relevant achievements like grades, awards, or leadership roles. See our adaptable education resume sample below.

Master of Science in Marriage & Family Therapy, University of Washington, Seattle September 2018 - May 2020

Bachelor of Science in Psychology, Western Washington University, Bellingham September 2014 - May 2018

If you have many publications, awards, memberships, or other honors to show, you may consider creating a separate section to call the reader’s attention to the achievements on your PhD resume. Here you can list the award or publication name, the publisher or granting institution, and the date completed. You may also create one or two bullet points to expand on each entry. If you don’t have many of these types of achievements to show, or if space is limited on your resume, you may consider including these accolades throughout the other sections of your resume. However, be sure that they are clearly highlighted so that the reader doesn’t miss your key attributes.

Another additional section you may need to include is academic references (called referees in the case of a PhD resume.) Don’t forget to double-check whether or not referees are actually required/requested on your resume. Otherwise, you’ll be wasting valuable space on a section that is less important to the section committee.

Resume layout and design

The right look and feel for your PhD resume is crucial to keeping the reader’s attention and proving that you are a serious applicant. Therefore, it’s important to make sure your layout and design are professional and tailored to the program you are applying for. 

An expertly-designed resume template can make it easy to create a great design with minimal headache. Look for a layout that clearly highlights your name and contact information and gives you enough space to include any additional sections like awards or referees. Stick with standard 1-inch margins and make sure to use the same font styles and sizes throughout your resume.

Key takeaways for a PhD resume

• A resume is an essential application document when applying for PhD programs but it will likely look different from resumes created for employment opportunities.
• Pay attention to the requirements of the PhD program and tailor your resume to match. The skills and summary section are great places to do this.
• Don’t forget to include PhD-specific information like your areas of interest, publications, awards, or referees.
• Keep your design professional and formal. Check out our adaptable resume sample to get started on creating a great layout.

Beautiful ready-to-use resume templates

25 Data Scientist Resume Examples and Templates for Your Successful 2024’s Job Search

Our Resumes are featured in

• • Developed and maintained data models to process purchase transaction data sets, improving procurement efficiency by 15%.
• • Created weekly reports on small-business achievements for multiple agencies, ensuring alignment with set financial targets.
• • Spearheaded the development of a public data tool, enhancing data accessibility for stakeholders and resulting in a 25% increase in usage.
• • Collaborated with cross-functional teams to represent data insights in various public policy settings, fostering informed decision-making.
• • Authored comprehensive technical reports, providing authoritative insights into GCBD initiatives and influencing policy formulation.
• • Coordinated and ensured the delivery of accurate, in-depth analysis reports on federal procurement processes.
• • Conducted robust statistical analyses on procurement data, providing actionable insights that helped identify cost-saving opportunities.
• • Designed and implemented data visualization dashboards, enhancing management's ability to track key performance metrics.
• • Compiled and analyzed weekly performance reports for 24 CFO act agencies, improving year-over-year performance tracking by 20%.
• • Collaborated effectively with peers and supervisors, ensuring cohesive team efforts and successful project completions.
• • Assisted in writing detailed program evaluation reports, offering in-depth understanding of GCBD initiatives and their impacts.

The brilliance of a Data Scientist is like a key that opens untold treasures of information. They are the unsung heroes who decipher the data patterns that shape our decisions. Your ability to make sense of numbers can propel entire industries forward.

Junior Data Scientist Resume Example

Junior Data Scientists often focus on analyzing data, building models, and presenting insights that help move a company forward. Your resume should highlight relevant internship experience, familiarity with statistical software, proficiency in SQL, and projects showcasing your analytical skills. Highlight proficiency in languages like Python or R, familiarity with databases, and some experience with machine learning algorithms. Make sure to mention your ability to work well in a team, effective communication skills, and a propensity for problem-solving. Remember, showcasing tangible results and impact from your previous work can set your resume apart from others.

Senior Data Scientist Resume Example

As a Senior Data Scientist, your main task is to manage complex data projects and extract meaningful insights that drive business decisions. Your resume should show your extensive experience with data interpretation, statistical analysis, and project management. Make sure to highlight your expertise in programming languages like Python or R, as well as knowledge in machine learning algorithms and big data tools like Hadoop or Spark. People skills such as effective communication and team collaboration are human qualities essential for this role. Above all, your Data Scientist resume should reflect a blend of technical prowess and an ability to translate data into actionable business strategies.

Lead Data Scientist Resume Example

Lead data scientists play a pivotal role within a company, guiding complex data projects and crafting insights that drive strategic decisions. When working on your resume, you should highlight key responsibilities such as developing machine learning models, managing data pipelines, mentoring junior data scientists, conducting predictive analytics, and collaborating with other departments. Your resume has to reflect technical skills and technologies, including proficiency in Python and R, expertise with SQL and Big Data tools, and experience with cloud platforms like AWS or Azure. Make sure you mention interpersonal skills, like exceptional communication abilities, leadership aptitude, and the knack for working well within a team structure. Remember, your data scientist resume should tell a story of your impact and accomplishments, not just your job descriptions.

Principal Data Scientist Resume Example

Principal Data Scientists take on the key role of leading data-driven initiatives and projects within a company, ensuring that data analytics align with business goals. Your resume should spotlight achievements like model development, data strategy implementation, and team leadership, alongside specific responsibilities managed. Highlight your expertise in programming languages such as Python or R, familiarity with big data tools like Hadoop, and advanced skills in SQL. Equally crucial, emphasize your communication abilities, teamwork, and leadership skills to show how you can effectively coordinate and guide multi-disciplinary teams. Remember that a strong Data Scientist resume not only underscores technical prowess but also showcases the ability to translate complex findings into actionable business insights.

Associate Data Scientist Resume Example

As an Associate Data Scientist, you're responsible for analyzing complex data to help your company make informed decisions. Your resume should include experience in data analysis, statistical modeling, and perhaps machine learning projects, showcasing your technical prowess. It's crucial to highlight your skills in programming languages like Python, R, and tools such as SQL and Hadoop. You should also emphasize your ability to communicate findings clearly, collaborate with team members, and demonstrate problem-solving abilities. Remember, a standout data scientist resume is one that balances technical skills with strong interpersonal abilities.

Director of Data Science Resume Example

You oversee the team shaping your company’s data-driven strategies as a Director of Data Science. Make sure your resume showcases your leadership skills, key projects you’ve led, and your impact on decision-making. Highlight your skills with tools like Python, R, SQL, and experience with machine learning frameworks. Don’t forget to mention your ability to communicate effectively, manage diverse teams, and nurture a collaborative atmosphere. Remember, the focus for a Data Scientist is on practical examples that showcase your analytical expertise and tangible outcomes.

Data Science Manager Resume Example

As a Data Science Manager, you're responsible for guiding your team to develop predictive models and analyze large datasets to help the company make data-driven decisions. Your resume should showcase your leadership experience, successful project management, and your proficiency in statistical analysis and machine learning. Highlight your expertise with tools like Python, R, SQL, and big data technologies such as Hadoop and Spark. Don't forget to include your ability to communicate complex ideas clearly and your knack for fostering teamwork and collaboration. Remember, your resume should tell a compelling story about your analytical achievements and leadership capabilities.

Data Science Specialist Resume Example

As a Data Science Specialist, your main role involves interpreting complex datasets to guide company decisions and strategies. Your resume should include specific projects you’ve led, quantifiable results achieved, and your ability to work collaboratively with different departments. Highlight your proficiency in languages like Python and R, as well as your experience with machine learning frameworks. Don’t forget to showcase your skills in communication, teamwork, and problem-solving, which are equally important. Remember, tailor your resume to clearly demonstrate how your unique skills have directly benefited past employers.

Chief Data Scientist Resume Example

Chief Data Scientists hold important roles, guiding their companies through data-driven strategies and innovations. Your resume should demonstrate leadership in analytics, data governance, and extensive experience in managing complex projects. To make a strong impression, highlight your skills in machine learning, data modeling, and familiarity with technologies like Python, R, and SQL. Your ability to communicate effectively, work well in teams, and solve problems creatively will set you apart. Always remember, your resume reflects not just technical prowess but also how you can drive change and growth.

Data Science Analyst Resume Example

A Data Science Analyst in a company is in charge of examining data and providing insights to support business decisions. Your resume should mention key responsibilities like data cleaning, statistical analysis, creating predictive models, and communicating findings to stakeholders. Highlight technical skills in programming languages such as Python or R, data visualization tools like Tableau, and proficiency in SQL. Include soft skills like problem-solving, communication, and teamwork to show you can work well with others and explain your findings clearly. Remember, showcasing real project experiences with specific outcomes is what will make your resume stand out.

Data Science Engineer Resume Example

Data Science Engineers drive key decisions in a company by analyzing vast amounts of data and building predictive models. Your resume should show your past work experiences, technical abilities, and how you contributed to the growth of previous employers. You must highlight hard skills, like proficiency in programming languages such as Python and R, as well as experience with machine learning frameworks and big data technologies. Don’t forget to mention your excellent teamwork, problem-solving abilities, and communication skills, as these are crucial when working with diverse teams. The main thing to keep in mind is to showcase specific achievements backed by data to truly stand out.

Business Intelligence Data Scientist Resume Example

As a Business Intelligence Data Scientist, you’re expected to transform raw data into insightful business strategies. Your resume should showcase your experience with data modeling, statistical analysis, and creating dashboards, while also highlighting your ability to work cross-functionally with decision-makers. Make sure to spotlight your expertise in SQL, Python, R, and machine learning algorithms. Don't forget to emphasize your strong communication skills, problem-solving abilities, and teamwork readiness. Always remember to quantify your achievements and detail the impact of your work to stand out.

Data Science Project Manager Resume Example

Data Science Project Managers guide teams through data-driven projects, turning raw data into actionable insights for better decision-making. Your resume should highlight your experience managing data projects, your ability to work with stakeholders, and your knack for keeping teams on track and deadlines met. Showcase technical skills in Python, SQL, machine learning, and data visualization tools like Tableau or Power BI. Don’t forget to list your strong communication abilities, leadership qualities, and your aptitude for collaboration and conflict resolution. Remember, a Data Scientist’s resume should be concise yet comprehensive, tailored to demonstrate the ability to turn data into strategic asset.

Clinical Data Scientist Resume Example

A Clinical Data Scientist in your company is the go-to person for analyzing medical data, developing algorithms, and ensuring accuracy in clinical studies. When you draft your resume, it should list responsibilities like data cleaning, statistical analysis, and collaboration with medical professionals. Make sure to highlight your proficiency in SQL, Python, and machine learning techniques. Your people skills must include strong communication abilities, teamwork, and problem-solving capabilities. Always remember, keep your resume concise yet detailed to stand out in a crowded field.

Health Data Scientist Resume Example

Health Data Scientists are critical in any company for analyzing health data and translating it into actionable insights. When you're crafting your resume, make sure to detail your experience with data cleaning, statistical analysis, and healthcare informatics. Highlight hard skills like Python, SQL, R, and machine learning technologies to show your technical prowess. Equally important, emphasize soft skills like communication, adaptability, and teamwork as these make you a well-rounded candidate. Always remember to tailor your resume to reflect the specific job description you're applying for, as this makes it easier for hiring managers to see your fit.

Data Science Instructor Resume Example

Data Science Instructors play a pivotal role in guiding and mentoring teams, helping them gain proficiency in data analysis techniques and best practices. Your resume should highlight your experience with designing educational programs, conducting workshops, and developing learning materials, along with collaborating with cross-functional teams. Make sure you spotlight your expertise in SQL, Python, R, and machine learning frameworks like TensorFlow or PyTorch. Don't forget to include soft skills such as excellent communication, problem-solving abilities, and leadership qualities that can captivate and inspire your students. Remember, showcasing your passion for teaching and ability to demystify complex topics is what will set your resume apart.

Data Science Researcher Resume Example

Data Science Researchers in a company are responsible for collecting, analyzing, and interpreting complex data to help drive decision-making and strategy. Your resume should highlight your analytical skills, experience with data modeling, proficiency in machine learning, and your ability to deliver actionable insights. When listing your hard skills, include your expertise in programming languages like Python or R, familiarity with big data tools like Hadoop, and your experience using statistical software such as SAS. Don’t forget to showcase your soft skills, such as your ability to communicate technical concepts to non-technical stakeholders, teamwork, problem-solving, and creativity. Remember, a standout resume for a Data Scientist should weave together both technical proficiency and the ability to tell a compelling data story.

Data Science Architect Resume Example

Data Science Architects play a pivotal role in your company, designing and implementing data solutions to solve your business challenges. Your resume should detail your experience in data architecture, analytics, and machine learning, as well as projects where you led data strategy initiatives. You'll want to spotlight hard skills in Python, R, SQL, and knowledge of cloud platforms like AWS or Azure. Don't forget to mention your ability to communicate complex ideas clearly, lead teams, and think strategically. Always remember, your resume must reflect real-world impact and measurable results.

Financial Data Scientist Resume Example

A Financial Data Scientist's main task is to analyze company data to uncover patterns and trends that can drive strategic decisions. In your resume, include your experience with data manipulation, machine learning, statistical analysis, and insights generation. Highlight skills in Python, R, SQL, and experience with data visualization tools like Tableau or Power BI. Equally important are your communication skills, problem-solving mindset, and the ability to collaborate with diverse teams. Remember, your resume should convey your unique contributions and how your skill set aligns with the company's needs.

Data Science Strategist Resume Example

Data Science Strategists are responsible for creating and guiding the data-driven strategies that drive company growth and decision-making. Make sure your resume lists responsibilities like data analysis, building predictive models, and collaborating with various departments to implement data-driven solutions. Highlight hard skills in machine learning, programming languages like Python or R, and experience with data visualization tools. Showcase your soft skills by emphasizing your ability to communicate complex ideas to non-technical stakeholders, collaborate efficiently, and manage time effectively. Remember, your resume should clearly convey your data intuition and how you’ve helped make impactful decisions.

Data Science Operations Analyst Resume Example

A Data Science Operations Analyst in a company is responsible for leveraging data to create actionable insights, optimize processes, and support decision-making. Their resumes should thoroughly reflect significant experience in data analysis, project management, automating reports, and proficiency in various data visualization tools. Highlighting hard skills like SQL, Python, R, and machine learning frameworks, along with a good grasp of big data technologies such as Hadoop and Spark, is vital. Additionally, showcasing excellent communication skills, teamwork, and the ability to translate complex data into comprehensible information for non-technical colleagues is just as crucial. The big takeaway is to demonstrate your ability to turn raw data into real business value efficiently and effectively.

Data Science Consultant Resume Example

Data science consultants help streamline and improve a company's decision-making processes by analyzing large volumes of data and providing actionable insights. It's important that their resumes list key responsibilities such as data analysis, statistical modeling, implementing machine learning algorithms, and presenting findings to stakeholders. Highlight your knowledge of programming languages like Python and R, proficiency with SQL, and experience with data visualization tools such as Tableau or Power BI. Don't forget to mention your ability to communicate complex concepts clearly, collaborate effectively with teams, and manage time efficiently. The big takeaway is that your resume should reflect not just your technical prowess but also how you can add tangible value to the business.

Machine Learning Data Scientist Resume Example

Machine Learning Data Scientists play a key role in a company by developing models and algorithms that analyze large datasets to derive actionable insights. You want your resume to detail specific responsibilities such as designing predictive models, collaborating with cross-functional teams, and optimizing machine learning algorithms for performance. Highlight your expertise in hard skills and technologies like Python, TensorFlow, SQL, and data visualization tools to show your technical proficiency. Emphasize your soft skills, such as communication, problem-solving, and teamwork abilities, which are equally important in collaborating with colleagues. Always remember, your resume should clearly reflect how your contributions drive business outcomes and provide measurable results.

Big Data Scientist Resume Example

Big data scientists help your company by analyzing large sets of data to drive insights and make decisions. Your resume should list previous experience with data analysis, model building, and managing complex datasets, with clear examples of your success in those areas. When drafting your resume, be sure to highlight your proficiency in Python, SQL, Hadoop, and machine learning algorithms. People skills like teamwork, communication, and the ability to simplify technical concepts for non-technical colleagues should be front and center. Finally, keep in mind that showcasing your problem-solving abilities through concrete examples will make your resume stand out.

Looking for more specific tips? Check all related jobs’ resume guides here:

• Junior Data Scientist resume
• Senior Data Scientist resume
• Lead Data Scientist resume
• Principal Data Scientist resume
• Associate Data Scientist resume
• Director of Data Science resume
• Data Science Manager resume
• Data Science Specialist resume
• Chief Data Scientist resume
• Data Science Analyst resume
• Data Science Consultant resume
• Machine Learning Data Scientist resume
• Big Data Scientist resume
• Data Science Engineer resume
• Business Intelligence Data Scientist resume
• Data Science Project Manager resume
• Clinical Data Scientist resume
• Health Data Scientist resume
• Data Science Instructor resume
• Data Science Researcher resume
• Data Science Architect resume
• Financial Data Scientist resume
• Data Science Strategist resume
• Data Science Operations Analyst resume

The most important tips for Data Scientist resumes:

Creating a compelling resume as a Data Scientist requires showcasing the right skills, format, length, and structure to make an impact on recruiters. Below are some key tips to help guide you.

Simple and effective format: Recruiters favor resumes that are easy to read and well-organized. Avoid creative distractions like fancy fonts, colors, and graphics. Stick to a clean, professional format with clear section headings.

Highlight your projects: Practical examples of your work make a significant difference. Detail a few key projects where you solved complex problems using data science techniques. Include the tools used and the measurable outcomes.

Provide statistical and analytical tools: List the tools and software you are proficient in, such as Python, R, SQL, TensorFlow. This includes libraries like pandas, NumPy, and scikit-learn. Match these to what the job listing mentions.

Emphasize your education correctly: Place your degrees higher up if they are highly relevant or recent. Also mention any certifications or online courses you completed. This showcases your commitment to continuous learning.

Customize for each application: Tailor your resume specifically for each job you apply to. Highlight and rearrange relevant skills and experience to match the job description. Personalizing your resume helps in grabbing the recruiter's attention quickly.

Must-Have Sections on a Data Scientist Resume:

Creating a standout data scientist resume requires including several main sections that showcase your skills, background, and achievements clearly. Each section plays a distinct role in persuading hiring managers that you are the right fit for the job.

• contact information: Your contact details should always appear at the top of your resume. Include your full name, phone number, email address, and location. This allows employers to reach out to you promptly.
• professional summary: Provide a brief overview of your career, skills, and key accomplishments. It sets the tone for the rest of your resume and grabs the reader's attention. This is your chance to make a strong first impression.
• technical skills: Listing your technical proficiencies gives hiring managers a quick insight into your capabilities. Include programming languages, data analysis tools, and any software you are proficient in. This section quickly demonstrates your technical competence.
• experience: Detail your previous job roles and responsibilities, focusing on achievements. Use bullet points to list your duties and the impact you had. This tells employers about your hands-on experience and proven track record.
• education: Mention your educational background, including degrees earned and institutions attended. This section validates your academic qualifications and any relevant coursework. It helps to establish your foundational knowledge in the field.

Apart from the main sections, adding supplementary sections can further strengthen your resume and highlight additional qualifications.

• projects: List significant projects you've worked on, including those completed during school or on your own. Describe the objectives of each project and the tools or methods used. This demonstrates your ability to apply your skills in practical scenarios.
• certifications: Include any certifications or specialized training you've completed. This could cover certifications in machine learning, big data, or other relevant areas. Certifications show your commitment to continued learning and expertise.
• publications: If you've published any research papers, articles, or case studies, mention them here. Include the titles, publication dates, and where they were published. This highlights your contributions to the field and your ability to share knowledge.

How to Write Your Data Scientist Resume Experience Section

Your resume’s work experience section needs to reflect your capabilities and achievements as a Data Scientist. It should convey not just the tasks you performed but the impact you had on your team and organization. Having a detailed and well-structured experience section can help you stand out to HR managers. Use these practical tips to highlight your strengths and showcase your Data Science journey in the best way possible.

• Begin each bullet point with a strong action verb to make your accomplishments stand out. Words like “developed,” “analyzed,” and “implemented” show initiative and leadership. They make each statement powerful and engaging.
• Critical to highlight specific projects or models you worked on, providing details like techniques and software used. Mentioning tools like Python, R, or SQL makes your technical skills evident. This gives a practical insight into your capabilities.
• Demonstrate your problem-solving skills by detailing challenges you faced and how you addressed them. This shows you can think critically and adapt in challenging situations. It makes your experience relatable and impressive.
• Always quantify your achievements to provide a clear context for your impact. Use metrics like “increased accuracy by 15%” or “reduced processing time by 30%”. This makes your accomplishments tangible and measurable.
• Emphasize collaboration and teamwork by mentioning projects completed with cross-functional teams. This highlights your soft skills and ability to work harmoniously with others. Companies value team players who can communicate effectively.
• Showcase your continuous learning and improvements by including recent certifications, courses, or workshops attended. It demonstrates a commitment to staying current with the latest industry trends. Continuous improvement is valued in tech roles.
• Include details on how your work benefited the company or clients. Did you save costs or drive revenue? This shows you’re not just a technical expert but also business-minded.
• If you implemented or suggested any innovations, indicate their effect on the team or project outcomes. Innovation drives progress and reflecting this on your resume can be very compelling. Show that you bring fresh ideas to the table.
• Tailor your experience to the job description, emphasizing skills and projects relevant to the specific role. It makes your application more personalized and attractive to recruiters. Tailoring is always noticeable and appreciated.
• When detailing your responsibilities, use industry-standard terminology to show familiarity with your field. This validates your expertise and aligns with what hiring managers are looking for. Industry language bridges your experience with expected job functions.

Next, we’ll provide examples of how to quantify your experience, what are the most common responsibilities HR managers look for in your resume, and how to align it with the job description more precisely. If you are aiming for an entry-level position, we will also discuss how to compensate for the lack of experience.

Examples of How To Quantify Your Experience

• Increased prediction accuracy of sales forecasts by 25% through the implementation of advanced machine learning models, directly contributing to a 15% rise in annual revenue.
• Developed a recommendation system that improved user engagement by 40%, resulting in a 20% increase in conversion rates within six months of deployment.
• Optimized data processing pipeline, reducing ETL runtime by 50% and enabling real-time data analytics capabilities, which reduced decision-making time by 30%.
• Conducted sentiment analysis on customer reviews, achieving an 85% accuracy rate, which informed marketing strategies and resulted in a 10% boost in customer satisfaction scores.
• Built and deployed a predictive maintenance model that decreased equipment downtime by 70%, generating $500,000 in savings for the company annually.
• Implemented a fraud detection algorithm that reduced false positives by 60% and cut operational costs related to manual reviews by 35%.
• Led a team to develop a customer segmentation model that identified 5 high-value customer groups, increasing targeted marketing campaign ROI by 25%.
• Devised an A/B testing framework that enabled product teams to test and iterate features twice as quickly, reducing time-to-market by 30% for new releases.
• Automated data quality checks that reduced data discrepancies by 90% and saved the data analytics team 20 hours per week in manual validation efforts.
• Created a dashboard that provided real-time business intelligence, leading to a 15% increase in operational efficiency and quicker strategic decision-making.
• Enhanced a churn prediction model, achieving 92% accuracy, which helped the customer success team reduce churn rate by 18% over a year.
• Conducted a comprehensive data audit that uncovered opportunities to save 10% on data storage costs and improved overall data governance.
• Developed a natural language processing tool that increased the accuracy of email categorization by 75%, leading to a 50% improvement in customer response times.
• Analyzed web traffic data, identifying key user behavior trends that informed website redesign efforts, increasing average session duration by 25%.
• Implemented a real-time fraud detection system that identified and mitigated 95% of potential fraudulent transactions, safeguarding $1 million in assets annually.

Job Description Bullet Points on Data Scientist Resumes:

• Design, implement, and optimize machine learning models to derive actionable insights from complex datasets and drive business decisions.
• Collaborate with cross-functional teams to understand requirements, deliver data-driven solutions, and support strategic initiatives.
• Develop and maintain scalable, automated analytics processes for large-scale datasets encountered in real-time application scenarios.
• Create and deliver data visualization dashboards and reports that effectively communicate key metrics and findings to stakeholders.
• Conduct exploratory data analysis (EDA) to identify trends, patterns, and anomalies that inform research hypotheses and guide model development.
• Implement and refine advanced statistical algorithms to improve the accuracy and efficiency of predictive analytics applications.
• Apply natural language processing (NLP) techniques to analyze and interpret unstructured text data, enhancing the depth of data insights.
• Develop and validate data models using a variety of statistical and machine learning techniques, ensuring robustness and reliability.
• Integrate disparate data sources, performing ETL processes to ensure data integrity and accessibility for analysis and modeling.
• Collaborate with IT teams to deploy machine learning models into production environments, ensuring scalability and performance.
• Prepare comprehensive documentation on methodologies, model performance, and data workflows to support transparency and reproducibility.
• Lead data-driven decision-making workshops and training sessions to empower non-technical stakeholders with analytical capabilities.
• Conduct A/B testing and multivariate experiments to measure the impact of product changes and optimize user experiences.
• Keep current with the latest developments in data science, machine learning, and AI, continuously integrating cutting-edge techniques into analyses.
• Provide mentorship and technical guidance to junior data scientists and analysts, fostering professional development and promoting best practices.

How to Tailor Your Data Scientist Resume To the Job Description:

• Use exact keywords from the job description in your work history, so recruiters instantly recognize the relevance. Describe your accomplishments using specific industry terms that the hiring manager will understand. Match your past experiences to their listed requirements clearly and concisely to grab their attention.
• Highlight your most impactful projects that align with the role, showing how you've handled similar tasks before. Quantify your results to demonstrate your capabilities. Be sure to depict how your contributions led to success within your previous organizations.
• Showcase your technical skills prominently by aligning them with those mentioned in the job posting. Mention any software, tools, or programming languages that align with their tech stack. Provide context by explaining how you’ve used these skills in past roles to achieve goals.
• Mention relevant data analysis techniques you've used that align with the job's expectations. Specific methodologies and tools that reflect the company’s needs will make your resume stand out. Share outcomes that were achieved through these methods to illustrate your effectiveness.
• Emphasize your collaborative projects and teamwork experiences, especially if the job involves cross-functional teams. Detail your role and how you contributed to the group's success. This showcases your ability to work well within a team dynamic.
• Stress your problem-solving acumen by providing examples where you identified issues and implemented data-driven solutions. Walk through the steps you took to resolve challenges. Highlight any innovative techniques or strategies you employed to improve processes or outcomes.

How to Write Your Resume Summary/Objective Section

Including a resume summary or objective for a Data Scientist can set the tone for your entire application. A resume summary is ideal for those with a few years of experience in the field, showcasing your proficiency and unique skills. On the other hand, a resume objective works well if you're entering the field, guiding employers to understand your aspirations and what you bring to the table. Choose the one that highlights your strengths effectively and aligns with the position you're targeting.

A summary succinctly captures your career highlights and skills in a few impactful lines, while an objective outlines your career goals and what you aim to achieve in the role. If you have substantial experience in data science, a summary can illustrate your past achievements and why you're the perfect fit. For those just starting or transitioning into data science, an objective can clarify your career intentions and eagerness to learn. Opt for a summary if you're aiming to underscore your experience and an objective if you're highlighting potential and dedication.

Practical tips for your resume’s summary:

• Highlight your unique technical skills in the first sentence to grab the attention of the hiring manager immediately. Mention any specific programming languages, tools, or methodologies that you excel in. This shows you're well-versed in the technical aspects of the job.
• Incorporate measurable achievements to demonstrate the impact you’ve had in previous roles. Quantifiable results such as 'increased sales by 20%' or 'reduced data processing time by 30%' make your skills more tangible. Numbers are appealing to potential employers.
• Use keywords relevant to the job description to pass through Applicant Tracking Systems (ATS). Tailor your summary to include skills and experiences the employer is seeking. This alignment can significantly boost your chances of being noticed.
• Reflect on the industries or business problems you have experience in to show broader expertise. Mentioning specific sectors like healthcare, finance, or e-commerce can convey versatility. It suggests you understand different industry challenges.
• Keep it concise yet detailed, ideally no more than 3-4 sentences. This brevity ensures that your summary is digestible and impactful. Conciseness forces you to focus on the most critical points, making each word count.
• Convey your enthusiasm and passion for data science subtly within the summary. Subtextually, enthusiasm can reveal through your proactive wording and confident tone. Employers often seek candidates who are genuinely excited about their field.

Let's move forward to providing actual examples that illustrate these points effectively.

Resume’s personal statement examples:

• Experienced Data Scientist with a strong foundation in statistical analysis, machine learning, and data visualization. Proven ability to transform complex datasets into actionable insights to drive business decisions.
• Results-driven Data Scientist specializing in predictive modeling and big data analytics. Adept at leveraging large datasets to identify trends, optimize performance, and support strategic initiatives.
• Multidisciplinary Data Scientist with expertise in Python, R, and SQL. Skilled in building data pipelines, developing machine learning models, and conducting A/B testing to enhance product features.
• Goal-oriented Data Scientist intern with a background in computer science and applied mathematics. Eager to apply academic knowledge to real-world problems and contribute to innovative data solutions.
• Detail-oriented Assistant Data Scientist passionate about data integrity and data mining. Enthusiastic about learning advanced analytical techniques and supporting senior data scientists in diverse projects.
• Innovation-driven Extern Data Scientist with hands-on experience in data preprocessing and exploratory data analysis. Highly motivated to gain further expertise in predictive analytics and data-driven decision making.

Top Resume Skills for Data Scientist

Data Scientists possess a blend of top skills, expertise, and competencies to excel in their field. They need to analyze complex data sets with precision. Strong programming skills are vital. Additionally, they should have the ability to communicate their findings effectively to non-technical stakeholders.

• Always highlight your proficiency in programming languages like Python, R, or SQL. Mention specific libraries or frameworks you've used effectively in past projects. This will give hiring managers a clearer picture of your technical capabilities.
• Showcase your experience with data visualization tools such as Tableau or Power BI. Describe specific projects where your visualizations played a key role in making important decisions. This shows your ability to turn data into actionable insights.
• Include your expertise in machine learning algorithms and models. Mention specific models you have built or improved. Potential employers want to see how you can apply these skills to real-world problems.
• Highlight your statistical analysis skills. Discuss specific techniques or methodologies you've employed. This exhibits your capability to understand, interpret, and derive insights from data.
• Mention your experience with big data technologies like Hadoop or Spark. Provide examples of how you've managed large datasets efficiently. This demonstrates your ability to handle and process huge amounts of data effectively.
• Include your soft skills such as communication and teamwork. Provide examples of how you’ve successfully worked with interdisciplinary teams. This shows you can collaborate and bring technical insights to a broader audience.

Now that you're ready to write the skills section of your resume, let's look at a list of important skills you might include:

Top Hard Skills for Data Scientist Resumes

• Machine Learning
• Deep Learning
• Data Visualization
• Data Mining
• Big Data Technologies
• Data Wrangling
• Natural Language Processing
• Mathematics

Top Soft Skills for Data Scientist Resumes

• Problem Solving
• Communication
• Critical Thinking
• Time Management
• Adaptability
• Attention to Detail
• Collaboration
• Self-motivation
• Decision Making
• Project Management
• Analytical Skills
• Interpersonal Skills

Include a Data Scientist Cover Letter for a Stand-Out Application

Cover Letter Writing Tips for Data Scientist Applicants

Writing a cover letter as a Data Scientist is crucial. It serves as a unique introduction to your skills and aspirations. The cover letter holds significant weight in job applications, often being the first point of contact with potential employers. Crafting a strong one can position you favorably in the hiring process.

Practical advice for your cover letter:

• Start with a compelling opening statement that highlights your enthusiasm for the position. Make sure to mention the specific job title and where you found the job posting. This sets the stage for the rest of your letter.
• Briefly touch on your educational background, especially if it includes relevant degrees or certifications. Mention any coursework or projects that showcase your data science skills. This builds credibility and aligns your background with the job requirements.
• Describe your experience with data science tools and technologies. Highlight specific software or programming languages you have used. Mention any successful projects or outcomes to demonstrate your expertise.
• Discuss a particular project or problem you have solved. Explain the data science techniques you employed and the impact of your work. This provides concrete examples of your problem-solving abilities.
• Address the company’s needs and how your skills can meet them. Research the company’s recent projects or challenges. Tailor your message to show how you can contribute specifically to their goals.
• Emphasize your soft skills, such as communication and teamwork. Data scientists often collaborate with other departments, so these skills are crucial. Provide examples of when you effectively communicated or worked in a team.
• Include any relevant certifications or ongoing education. Mention platforms like Coursera or Udacity if you have completed courses there. This shows your commitment to continuous learning in the field.
• Highlight your analytical and critical thinking skills. Mention any situations where you had to analyze data to make decisions. This underlines your ability to handle complex datasets and extract actionable insights.
• Wrap up with a strong closing statement. Reiterate your interest and enthusiasm for the role. Thank the reader for considering your application and express your eagerness to discuss your application further.
• Proofread your cover letter for any errors. Have someone else review it as well. A polished cover letter reflects your attention to detail and professionalism.

Next, let’s explore how to align your cover letter with your resume:

Frequently Asked Questions

Should my data scientist resume be one page or longer.

Your Data Scientist resume should ideally be one page if you have less than 10 years of experience. This ensures that the most relevant information is conveyed succinctly to hiring managers who often skim through numerous resumes. A one-page resume can effectively highlight your key skills, projects, and achievements without overwhelming the reader. However, for those with significant experience or numerous publications, extending the resume to two pages can be acceptable, as long as the content is concise and relevant.

What is the best format for a Data Scientist resume?

A reverse-chronological format is generally the best choice for a Data Scientist resume. This format emphasizes your most recent and relevant experience by listing your job history in descending order, starting with your current or most recent position. This approach helps employers track your career progression and quickly assess your suitability for the role. It is especially beneficial for experienced professionals, as it showcases growth and relevant achievements in a clear, straightforward manner.

What should I highlight on my Data Scientist resume to stand out?

To distinguish your Data Scientist resume from others, emphasize your hands-on experience with data analysis, machine learning models, and statistical methods. Highlight specific projects where you have successfully applied these skills to solve real-world problems. Including metrics that demonstrate the impact of your work, such as improved prediction accuracy or cost savings, can also make a strong impression. Additionally, showcasing your proficiency in tools and programming languages like Python, R, SQL, and your ability to work with large datasets, can further set you apart.

What are some action verbs I should use on my Data Scientist resume?

Using strong action verbs can bring your Data Scientist resume to life. Words like 'analyzed,' 'developed,' 'implemented,' 'optimized,' 'designed,' 'evaluated,' and 'predicted' convey a sense of initiative and accomplishment. These verbs not only describe what you did but also imply how you contributed to your previous roles, thereby creating a more compelling narrative of your professional experience.

For more inspiration, why not check out our free resource of job-focused resume examples?

Cost Estimator

Cost Estimators are the unsung heroes of the business world. They bring clarity and foresight to projects, ensuring that budgets are maintained and visions are realized. Without their expertise, financial planning would be a shot in the dark. These professionals possess an analytical mindset and exceptional attention to detail. Employers appreciate their ability to assess costs, manage financial risks, and make informed decisions. A strong Cost Estimator resume must highlight these skills to attract the right opportunities.

Financial Executive

As a financial executive, crafting a strong resume is crucial in showcasing your skills and experience to potential employers. In 2024, there are a few key considerations to keep in mind when formatting your resume. Here's what you need to be aware of: Resume Length While there used to be a standard rule of sticking to a one-page resume, the landscape has evolved. For financial executives with extensive experience, a two-page resume is acceptable, as it allows you to provide sufficient detail about your accomplishments. Be sure to prioritize the most relevant information and keep it concise. Design and Format In 2024, it's important to have a clean and modern design for your financial executive resume. Stick to a professional font and utilize consistent formatting throughout. Consider using bullet points to highlight key achievements and make the document easy to skim for busy hiring managers. When it comes to the sections of your financial executive resume, there are a few essential ones to include: Summary Statement: A concise overview of your skills, experience, and accomplishments. Professional Experience: Highlight your previous roles and responsibilities, focusing on your achievements and quantifiable results. Education: Include your educational background, certifications, and any relevant coursework. Skills: List the key skills that are essential for a financial executive, such as financial analysis, strategic planning, and team management. Achievements: Showcase any notable achievements or awards that demonstrate your expertise and contributions. In addition to these core sections, there are a few optional but impactful sections you can consider: Professional Associations: If you are a member of any industry-related associations, mention them to showcase your involvement and commitment. Languages: If you are fluent in multiple languages, it can be beneficial to highlight this skill. Publications or Presentations: If you have authored any articles or presented at conferences, include them to showcase your expertise in the field. When describing your experience as a financial executive, using the Context-Action-Result (CAR) framework can help you effectively communicate your accomplishments. Here are a few examples of bullet points using this framework: [Context] Implemented a new financial forecasting system to improve accuracy and efficiency. [Action] Led a cross-functional team to evaluate and select the suitable software, conducted training sessions for the finance team, and developed standardized processes. [Result] Reduced forecasting errors by 20% and streamlined the budgeting process, resulting in a cost savings of $500,000 annually. [Context] Developed and executed a comprehensive financial strategy to support company growth. [Action] Conducted in-depth financial analysis, identified areas for improvement, and implemented cost-saving measures. [Result] Increased annual revenue by 15% and improved profit margins by 10% within the first year. When crafting your financial executive resume for 2024, remember these key takeaways: Keep your resume length to two pages, prioritizing the most relevant information. Utilize a clean and modern design with consistent formatting. Include essential sections like a summary statement, professional experience, education, skills, and achievements. Consider optional sections like professional associations, languages, and publications to make your resume stand out. Use the CAR framework to highlight your accomplishments effectively. By following these guidelines, you can create a powerful financial executive resume that showcases your skills and positions you as a top candidate in 2024.

Sales Support Coordinator

When it comes to creating a resume for a sales support coordinator position in 2024, there are a few key points to keep in mind. These include the length, design, and format of your resume. By following these guidelines, you can ensure that your resume stands out from the competition. Resume Length As a general rule, keeping your resume concise and to the point is crucial. Aim for a one-page resume, especially if your experience is less than 10 years. Recruiters have limited time to review each application, so by providing a clear and concise document, you increase your chances of grabbing their attention. Resume Design The design of your resume should be professional yet visually appealing. Avoid using overly complex templates or excessive colors and graphics that might distract from the content. Instead, opt for a clean and organized layout that is easy to read. Utilize bullet points and white space to make your resume skimmable and visually pleasing. Resume Format There are several popular resume formats to choose from, including chronological, functional, and combination formats. For a sales support coordinator position, the chronological format is often the most effective choice. This format highlights your work experience and achievements in reverse chronological order. Start with your most recent position and work your way back through your career history. This allows recruiters to easily see your relevant experience and progression within the field. While the specific sections you include may vary based on your experience and the job posting requirements, here are some popular sections to consider for a sales support coordinator resume: Contact Information: Include your full name, phone number, email address, and LinkedIn profile. Summary/Objective: Provide a brief overview of your skills, experience, and what you can bring to the role. Work Experience: Highlight your relevant work experience in reverse chronological order, including job titles, company names, dates of employment, and key responsibilities and achievements. Skills: In this section, list your technical skills, computer proficiency, and any relevant certifications or training. Education: Include your highest level of education, degree, institution, and graduation year. Awards/Achievements: If you have received any recognition or awards that are relevant to the sales support coordinator role, include them in this section. Additional Sections: Depending on your background and relevant experience, you may also include sections such as languages spoken, professional affiliations, or volunteer work. When describing your experience on your sales support coordinator resume, it is important to follow the Context-Action-Result (CAR) framework. This structure allows you to clearly demonstrate your skills and accomplishments in a concise and impactful manner. Here are some examples of bullet points using the CAR framework: Context: Managed and resolved customer inquiries and concerns through effective communication. Action: Provided timely and accurate information to sales team members, ensuring seamless coordination and support. Result: Increased customer satisfaction by 20% and contributed to a 15% increase in sales revenue. Context: Developed and implemented sales support processes to streamline operations and improve efficiency. Action: Collaborated with cross-functional teams to identify bottlenecks and implement solutions. Result: Reduced response time by 30% and increased overall team productivity by 25%. To summarize, here are some key takeaways when creating your sales support coordinator resume: Keep your resume concise: Aim for a one-page resume for maximum impact. Focus on a clean and professional design: Avoid overly complex templates or excessive colors and graphics. Utilize the chronological format: Highlight your work experience in reverse chronological order. Include relevant sections: Contact information, summary/objective, work experience, skills, education, awards/achievements, and additional sections. Follow the CAR framework: Use the Context-Action-Result structure to highlight your accomplishments. By following these tips and structuring your resume effectively, you can increase your chances of landing a sales support coordinator position in 2024.

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PhD Programme in Data Science

Admission requirements.

Applicants seeking admission to the PhD programme should satisfy the following minimum entrance requirements.

be a current MPhil student in the University who is seeking transfer to a PhD programme; or hold a higher research degree (or equivalent qualification) from a recognised university; or hold a Master’s degree (or equivalent qualification) from a recognised university; or hold a Bachelor’s degree with first-class honours (or equivalent qualification) from a recognised university.

English Proficiency Requirements

Applicants from an institution where the language of teaching is not English should satisfy the minimum English proficiency requirements as follows:

• a minimum TOEFL total score of 550 (paper-based test) or 79 (Internet-based test); or 59 (revised Paper-delivered test); or
• a minimum overall band score of 6.5 in IELTS; or
• other test scores that may be regarded as equivalent to TOEFL 550 (paper-based test) or 79 (Internet-based test); or 59 (revised Paper-delivered test).

College English Test (CET) is  NOT  accepted as an equivalent English proficiency test for Hong Kong PhD Fellowship Scheme (HKPFS).  For applicants with strong academic and research potential who have only taken CET-6 with a minimum score of 490, special consideration may be made on a case-by-case basis during normal round admission. 

Applications for the Master of Philosophy (MPhil) programme are not considered.

Hong Kong PhD Fellowship Scheme (2024/25)

The Hong Kong PhD Fellowship Scheme , established by the Research Grants Council (RGC) of the HKSAR government, aims to recruit the brightest students in the world to pursue their PhD studies in Hong Kong. 300 PhD Fellowships will be awarded in the 2024/25 academic year. While academic excellence is the primary consideration, applicants should demonstrate research ability/potential, communication and interpersonal skills, and leadership abilities. The result of the HKPFS 2024/25 will be announced in April/May 2024 by RGC.

The Fellowship awardee will receive:

• an annual stipend of HK$331,200 (~US$42,460).
• conference and research-related travel allowance of HK$13,800 (~US$1,760) per year for a maximum of three years.
• Chow Yei Ching School of Graduate Studies Entrance Scholarships around HK$89,496 (~US $11,473) which covers student's full-time tuition fees and on-campus hostel accommodation expenses in the 1st year of research studies.

For Fellowship awardees who are admitted to a 4-year PhD programme, CityU will provide a monthly studentship at the same level as the RGC Fellowship for their fourth year of study.

Click here to download leaflet

How to Apply

1.  Getting Started Select area of interest, contact  potential supervisor  and seek consent Observe application deadlines

2.  Prepare Take required tests before application deadline Prepare supporting documents (example: transcript, research proposal) Academic referee reports

3.  Apply Apply Now   (You are required to list all post-secondary institutions you have attended.)

• Submit initial application as early as possible to ensure that you have sufficient time to submit application to CityU.
• Applications which are not selected for the Hong Kong PhD Fellowship Scheme will be considered as normal applications.

Application Timeline

Supporting Documents

Remarks: Hard copies of the documents are required for further verification only if you are given an offer. Documents which are not in English should be accompanied by a formally translated version in English.

Selection Criteria

While the academic excellence is of prime consideration, the Review Panels will take into account, but not limited to, the following yardsticks for the selection of candidates:

• Academic excellence;
• Research ability and potential;
• Cultural diversity;
• Communication and interpersonal skills;
• Leadership abilities and societal responsibility;
• Institutional support.

Shortlisted candidates will be invited to admission interview (either face-to-face or via online means e.g. Zoom, Skype, etc.).

Programme Structure

1) Coursework Plan To be eligible to be awarded an MPhil/PhD degree, students are required to complete the following coursework requirements and to submit a thesis. MPhil: 7 credit units (including at least 2 credit units of research methodology and ethics course at postgraduate level); --> PhD: 20 credit units (including at least 9 credit units of core courses and at least 2 credit units of research methodology and ethics course at postgraduate level) And 1 credit unit compulsory course: Teaching Students: First Steps (SG8001). Individual students with insufficient English proficiency may be required to take a 1 credit unit course English as Medium for Instruction (SG8002) before they are allowed to enrol in SG8001. The credit unit earned from SG8001 will not be counted towards the minimum coursework requirement. And Training on Research Integrity (CITI Programme) .

Please refer to the programme requirements for PhD in Data Science at http://www.cityu.edu.hk/catalogue/pg/202324/programme/DS_P.htm. https://www.cityu.edu.hk/catalogue/pg/202223/programme/DS_M.htm . --> 2) Credit Transfer and Exemption Students who possess postgraduate qualifications of relevance to their research studies may apply for credit transfer or coursework exemption. At least half the coursework (4 credit units for MPhil and 7 for PhD) should be taken at CityU or other local institutions recognised under the Cross-institutional Course Enrolment Scheme. Credit transfer/coursework exemption should be limited to a maximum of 3 credit units for MPhil and 7 for PhD. Recommendations on credit transfer/coursework exemption require the approval of the School Dean.

3) Cross-departmental Course Registration Students who wish to take courses offered by Departments/Schools outside their host School should obtain approval from the offering Department/School before submitting the Form SGS16A/SGS16B to their host School for endorsement and approval. A list of approved courses and the syllabi are available for reference on the  Chow Yei Ching School of Graduate Studies (SGS)  website.

Scholarship and Funding

Chow Yei Ching School of Graduate Studies Entrance Scholarships  (Applicable to government-funded students only)

CityU offers Chow Yei Ching School of Graduate Studies Entrance Scholarships to encourage outstanding international students to undertake MPhil or PhD studies at the University with a view to promoting academic exchange and enhancing the international mix of the University’s student population.

The Scholarship covers students’ tuition fees and on-campus hostel accommodation expenses in their first year of their research studies (equivalent to approximately HK$89,496 (~US$11,474)). If granted a Scholarship, the above-mentioned expenses will be off-set by the award.

Tuition Waiver for Local Research Postgraduate (RPg) Students

The University Grants Committee (UGC) has introduced a ‘Tuition Waiver Scheme’ (‘The Scheme’) for all local students enrolled in full-time UGC-funded Research Postgraduate (RPg) programmes in local institutions, to cover their tuition fees with effect from the academic year 2018-19 (i.e. from July 1, 2018).

This Scheme provides non-means-tested funding to all current and new local students from all disciplines of study enrolled in full-time UGC-funded RPg programmes. Eligible students do not need to apply for the Scheme.

For more information of Tuition Waiver Scheme for Local Research Postgraduate Students, please refer to  http://www.ugc.edu.hk/eng/rgc/funding_opport/tws.html

For the full list of scholarships and financial aid, please visit  https://www.cityu.edu.hk/pg/research-degree-programmes/scholarships-financial-aid-and-fees .

Tuition/Continuation Fees

(normally adjusted in September every year)

Potential Supervisors: Potential applicants are encouraged to contact a  potential supervisor  prior to submitting an application to our University, though it is not compulsory.

Research Degree Coordinator: Professor Qi WU

General Enquiries: Tel: 3442 7887 Email:  [email protected]

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