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What is a Research Scientist?

Learn about the role of Research Scientist, what they do on a daily basis, and what it's like to be one.

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Definition of a Research Scientist

What does a research scientist do, key responsibilities of a research scientist.

• Designing and implementing rigorous experiments to test hypotheses and solve complex problems.
• Collecting, analyzing, and interpreting data using statistical techniques and specialized software.
• Writing research papers, reports, and reviews for publication in scientific journals and presentations at conferences.
• Applying for funding and grants to support research projects and initiatives.
• Collaborating with interdisciplinary teams of scientists and professionals to enhance research quality and applicability.
• Staying current with the latest scientific advancements and literature in their field of expertise.
• Developing and testing new scientific methods and technologies to improve research efficiency.
• Mentoring and supervising junior researchers, technicians, and graduate students.
• Ensuring all research activities are conducted in compliance with ethical and regulatory standards.
• Reviewing and providing feedback on the work of peers to validate research findings and proposals.
• Communicating with stakeholders, including industry partners, government agencies, and academic institutions.
• Translating research discoveries into practical applications and products for industry or societal use.

Day to Day Activities for Research Scientist at Different Levels

Daily responsibilities for entry level research scientists.

• Conducting experiments and recording detailed observations
• Assisting with literature reviews and data collection
• Performing basic data analysis and interpretation
• Maintaining laboratory equipment and ensuring supplies are stocked
• Participating in lab meetings and presenting findings
• Complying with lab safety protocols and regulatory requirements
• Receiving training in research methodologies and best practices

Daily Responsibilities for Mid Level Research Scientists

• Designing and leading their own experiments or sub-projects
• Writing grant proposals and securing funding for research
• Authoring and co-authoring scientific papers and reports
• Presenting research findings at conferences and seminars
• Collaborating with cross-functional teams within and outside the organization
• Mentoring entry-level scientists and research assistants
• Contributing to the development of research strategies and objectives

Daily Responsibilities for Senior Research Scientists

• Leading and managing major research projects and collaborations
• Developing and directing research strategies and priorities
• Mentoring and supervising mid-level scientists and research teams
• Securing substantial funding and managing budgets for research activities
• Establishing partnerships with industry and academia
• Advising on policy and contributing to the broader scientific community
• Reviewing scientific manuscripts and serving on editorial boards

Types of Research Scientists

Theoretical research scientist, experimental research scientist, clinical research scientist, data research scientist, applied research scientist, environmental research scientist, what's it like to be a research scientist , research scientist work environment, research scientist working conditions, how hard is it to be a research scientist, is a research scientist a good career path, faqs about research scientists, how do research scientists collaborate with other teams within a company, what are some common challenges faced by research scientists, what does the typical career progression look like for research scientists.

How To Become a Research Scientist in 2024

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Definition of 'research scientist'

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Research Scientist

Key skills:

• Bachelor’s or Master’s Degree in a relevant field ( Higher Degree valuable).
• Subject expertise
• Experience relevant to the field in either a research or lab environment
• Well-developed communication skills, both written and oral
• Self-motivated; able to work independently and under pressure
• Able to integrate into a research group

Typical job titles: Research Scientist, Scientist, Investigator, Specialist

In some research institutions or industrial research organisations, professional research roles can be called Scientist, Staff Scientist or Research Officer.

The Research Scientist or Research Officer role is essentially similar to that of a Postdoctoral Researcher or a Research Fellow in a university. It focuses on research but in the context of a research institute/organisation not a university, so there is no (or very limited) teaching of students. So a scientist can focus more on research, although there will always be some administrative work too.

A Research Scientist or, more likely, a Senior Scientist may act as a Principal Investigator (PI) or Co-Investigator to lead a research project, in just the same way as a university researcher.

Roles exist at a range of levels from Junior Research or Scientific Officer, Scientist, through to Senior Scientist or Staff Scientist. More management responsibilities come with progression, such as supervising staff and managing budgets.

Qinqin Huang

Staff Scientist

Qinqin Huang moved from China to Australia in 2015 for her PhD, and moved to the UK in 2019 to start her postdoc in medical genomics at the Wellcome Sanger Institute. She has been promoted to a Staff Scientist position and taken more responsibilities in the group.

The earlier you can get to grips with the landscape and requirements of STEM careers, the better.

What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

Cite this chapter

You have full access to this open access chapter

• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  

Part of the book series: Research in Mathematics Education ((RME))

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Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

You have full access to this open access chapter,  Download chapter PDF

Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

Agnes, M., & Guralnik, D. B. (Eds.). (2008). Hypothesis. In Webster’s new world college dictionary (4th ed.). Wiley.

Google Scholar  

Britannica. (n.d.). Scientific method. In Encyclopaedia Britannica . Retrieved July 15, 2022 from https://www.britannica.com/science/scientific-method

Brownell, W. A., & Moser, H. E. (1949). Meaningful vs. mechanical learning: A study in grade III subtraction . Duke University Press..

Cai, J., Morris, A., Hohensee, C., Hwang, S., Robison, V., Cirillo, M., Kramer, S. L., & Hiebert, J. (2019b). Posing significant research questions. Journal for Research in Mathematics Education, 50 (2), 114–120. https://doi.org/10.5951/jresematheduc.50.2.0114

Article   Google Scholar  

Cambridge University Press. (n.d.). Hypothesis. In Cambridge dictionary . Retrieved July 15, 2022 from https://dictionary.cambridge.org/us/dictionary/english/hypothesis

Cronbach, J. L. (1957). The two disciplines of scientific psychology. American Psychologist, 12 , 671–684.

Cronbach, L. J. (1975). Beyond the two disciplines of scientific psychology. American Psychologist, 30 , 116–127.

Cronbach, L. J. (1986). Social inquiry by and for earthlings. In D. W. Fiske & R. A. Shweder (Eds.), Metatheory in social science: Pluralisms and subjectivities (pp. 83–107). University of Chicago Press.

Hay, C. M. (Ed.). (2016). Methods that matter: Integrating mixed methods for more effective social science research . University of Chicago Press.

Merriam-Webster. (n.d.). Explain. In Merriam-Webster.com dictionary . Retrieved July 15, 2022, from https://www.merriam-webster.com/dictionary/explain

National Research Council. (2002). Scientific research in education . National Academy Press.

Weis, L., Eisenhart, M., Duncan, G. J., Albro, E., Bueschel, A. C., Cobb, P., Eccles, J., Mendenhall, R., Moss, P., Penuel, W., Ream, R. K., Rumbaut, R. G., Sloane, F., Weisner, T. S., & Wilson, J. (2019a). Mixed methods for studies that address broad and enduring issues in education research. Teachers College Record, 121 , 100307.

Weisner, T. S. (Ed.). (2005). Discovering successful pathways in children’s development: Mixed methods in the study of childhood and family life . University of Chicago Press.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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Academic Research

Raysonho/ Wikimedia Commons. Academic research can be intense, stimulating, and rewarding. But it is important to know that a research career involves many activities besides research. Scientists spend their time writing applications for funding to do research, as well as writing scientific papers to report the findings of their research. In addition, they spend time presenting their research in oral or poster form to other scientists at group meetings, institutional meetings, and scientific conferences; they also spend time teaching students about their field of study. A scientist's life is often full of tasks that need to be done and most scientists work very hard, but they also love what they do.

Fields of Study

• Clinical Scientist: David Fredricks
• Epidemiologist: Gloria Coronado
• Geneticist: Katie Peichel
• Clinical Research: Dana Panteleef
• Research Technician: Nanna Hansen

If you're interested in a general sense in academic research, the first thing to figure out is which field of research is best for you.

The fundamental task of research is asking questions. There are many areas of research in the life sciences, and they generally fall into three categories based on the types of questions that are asked and the tools that are used to answer the questions:

Basic Research

Clinical research, population-based research.

Basic researchers ask questions about how fundamental life processes work. Examples of questions include the following:

• What are the mechanisms that determine how and when cells divide?
• How do DNA mutations associated with a disease occur?
• How and why do cells age?
• How and why does one type of cell work differently from another type of cell?

Basic researchers usually work in laboratories with other scientists, usually with one faculty member leading a group of postdoctoral fellows, graduate students, and lab technicians who do most of the lab work. The hours can be very long and the work can be challenging, especially for graduate students and postdoctoral fellows. Basic researchers often ask their questions using model organisms, including yeast, worms, flies, fish, and mice.

• Scientific Recruiter: Scott Canavera
• Staff Scientist: Tom Paulson
• Shared Resources: Julie Randolph-Habecker
• Faculty Member: Wendy Leisenring

Clinical researchers ask questions about how disease occurs and how it can be cured in humans. Examples of questions include the following:

• How can we manipulate the body's immune system to improve treatment of a disease?
• How can we create a drug to improve disease survival?
• What are the long-term impacts of treatment on quality of life?

Clinical researchers work in laboratories that are very similar to basic researchers, but they often work with human tissue samples to ask their questions. Many clinical researchers find it rewarding to work on a question that may have an impact that they will eventually see come to fruition. At the same time, when you're working with human tissue, you usually have a limited amount of it so the risks of making a mistake that will lose your sample could be high. Clinical researchers will often collaborate with biostatisticians to best design and analyze their studies in order to yield the maximum amount of relevant information.

Population-based research is done by epidemiologists who ask questions to determine how diet, genetics, and lifestyle may influence the risk of disease. They ask these questions in one of two ways:

• by following a group of people over time and correlating exposure to who gets a disease;
• by asking a group of people with a disease about their lifestyle and diet choices and comparing the data to a randomly chosen group without the disease in order to look for differences between the two groups.

The types of questions they ask include the following:

• How can we best prevent teenagers from starting to smoke?
• Do some genetic variants place a person at greater risk for cancer?
• Do vitamins help prevent cancer?
• Does exposure to certain chemicals increase the risk of getting a particular disease?

Epidemiologists also collaborate with biostatisticians in order to design and analyze studies so they can get the most information from them. Rather than work in a lab, epidemiologists often need no more than a desk and computer. However, the interdisciplinary field of molecular epidemiology is changing this, and many epidemiologists ask questions about how a particular gene can influence disease risk, rather than, or in addition to, a lifestyle exposure.

Roles in Research

Faculty member.

Faculty members usually have Ph.D.'s or M.D.'s and have gone through graduate school or medical school followed by several years of being a postdoctoral fellow or medical resident. A faculty member is the leader of their own lab or work group and determines the direction of the research in their group. Most faculty members spend a good deal of their time writing grant proposals and manuscripts, reading research papers, reviewing colleagues' manuscripts and grant proposals, thinking and talking with others about their research to gain new ideas, and mentoring the people in their group.

Faculty positions are usually very competitive to get and are often a result of hard work over many years. However, most faculty members love what they do and wouldn't trade it for anything.

Research Scientist

Shared resource specialist, technician and other support staff, administrative positions.

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How to Become a Research Scientist

Industry Advice Science & Mathematics

Professionals with a background in biotechnology can choose to pursue many lucrative careers . One of the most common choices is to become a research scientist. These individuals work in drug and process development, consistently conducting research and performing experiments to help move the biotechnology industry forward. 

“At the highest level, a research scientist is somebody who can design and execute experiments to prove or disprove a hypothesis,” says Jared Auclair , director of the biotechnology and bioinformatics programs at Northeastern. “Within the world of biotechnology, that can mean a number of different things, from creating new drugs to improving the process of how we make a drug.”

Professionals in this industry are often drawn to the wide array of applications of this work, as well as the consistently positive career outlook. The average salary of a biotechnology research scientist is $85,907 per year, with plenty of opportunities for increased salary potential depending on specializations, location, and years of experience. 

These factors—alongside the growing demand for advancement in biotechnology over the last few decades—have led many aspiring biotechnologists to consider a career in research science. Below we offer five steps professionals can take to kick-start a career in this field.

Download Our Free Guide to Advancing Your Biotechnology Career

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5 Steps to Become a Research Scientist

1. acquire the necessary technical skills..

According to Auclair, there are four main applications of research science within the biotechnology field:

• Molecular Biology
• Process Science
• Biochemistry
• Analytical Biotechnology

Professionals hoping to pursue a career in research science must begin by deciding which of these four areas is the best fit for their interests and backgrounds. They must then acquire the specific skill sets they need to excel in that area. 

Below, Auclair breaks down some of the key skills and knowledge required within each of these specializations:

• Molecular biologists should focus on developing a complex understanding of DNA and learn how to do a Polymerase Chain Reaction alongside other DNA-related experiments. 
• Process scientists must understand cell biology and how to work with living mammalian cells, as well as how to perform analytical experiments using mass spectrometry and other analytical tools.
• Biochemists should focus on obtaining the skills necessary to make a protein drug, including the expression and purification of proteins.
• Analytical biotechnicians must become comfortable with techniques like mass spectrometry—a process that uncovers what drug products are at a molecular level.

One efficient way aspiring research scientists can obtain these specific skill sets is to pursue a master’s degree in biotechnology at a top university like Northeastern. 

“The biotech program is designed in collaboration with industry so that we’re meeting their needs,” Auclair says. “This includes training students with the skills they need to be a successful research scientist.”

The curriculum of Northeastern’s program explores the core competencies required to excel in the general biotechnology field and provides students with the unique subsets of skills they need to specialize in a specific area of research science. Students can even declare one of 10 industry-aligned concentrations, including options that directly relate with these common research science roles.

“Especially in industry, most people who are doing research science—who are actually doing the experiments and helping think about experiments with some of the senior leaders in the company—are people with a master’s degree,” Auclair says.

2. Become a critical thinker.

Alongside honing technical skills, Auclair says that critical thinking abilities are key for aspiring research scientists. 

“It’s important to become a critical thinker and a problem solver, and to challenge yourself wherever you can to step outside of your comfort zone,” Auclair says. 

Though critical thinking is a common requirement among most professional career paths, it is especially important for research scientists, who are constantly tasked with innovating and thinking creatively to solve problems.

Northeastern’s master’s in biotechnology program is designed to help students grow in this regard. “Everything we do within the program is geared [toward] making you a critical thinker and a problem solver,” Auclair says. “We try to define classes and assessments to make you think, [and] we also hire most of the faculty in our program directly from the industry, so they bring with them real-world experience that they can talk about with the students.”

These real-world case studies are a core component of Northeastern’s approach to learning, and they help prepare students to think critically about their work. By bringing this exposure into the classroom, students also graduate better prepared to tackle current industry challenges and adapt to evolving trends .

3. Hone your “power skills.”

It’s no longer enough for research scientists in biotechnology to have obtained the technical skills needed to complete their work. Today, many employers require an array of industry-specific “power skills”—previously known as “soft skills”—among candidates for research science roles.

Below we explore the top three “power skills” for biotechnology research scientists:

• Communication: As a research scientist, “you must be able to communicate scientific information to both technical and non-technical people,” Auclair says. For this reason, professionals should work to hone their verbal and written communication styles, focusing specifically on the variances in each depending on which audience they’re interacting with.
• Presentation Ability: Research scientists must be able to present their findings clearly and concisely to a variety of different audiences, ranging from fellow scientists to investors to C-suite executives. Research scientists must be comfortable in front of a group and know how to speak about their experiments and conclusions in an engaging and informative way.
• Teamwork: Although one might think a research scientist’s work is very siloed, today’s professionals must be very comfortable working with others in a lab environment. They must become comfortable sharing ideas, providing feedback to others in their cohort, and tweaking their experiments based on contributed findings.

Northeastern offers students the chance to explore each of these core “power skills” during their time within the master’s in biotechnology program. For example, the university offers countless opportunities for students to collaborate with and present to classmates, instructors, and even industry-leading organizations through Northeastern’s experiential learning opportunities, giving them the chance to apply these skills in both classroom and real-world situations early on.

Learn More: How to Become a Biotechnologist: Build Your Soft Skills

4. Obtain hands-on experience.

One of the most effective ways an aspiring research scientist can prepare for a career in this field is to obtain experiences working in a real lab. While finding these kinds of opportunities can be difficult for those just breaking into the field, programs like Northeastern’s MS in biotechnology bake hands-on learning directly into the curriculum. 

“Students do essentially four to six months [working in the] industry, and put what they learn in the classroom…into practice,” Auclair says.

These opportunities, known as co-ops , provide students with the chance to work within top organizations in the industry and explore the real-world challenges of the field from inside a functioning lab.

Did You Know: Northeastern’s program provides students with exposure to the tools and equipment used within labs in the industry. This access to cutting-edge technology reduces the learning curve and allows students to dive into their work as soon as they graduate.

Another unique way Northeastern provides hands-on experience is through Experiential Network (XN) Projects . Students who participate in these projects are typically paired with a sponsor from an active biotech company that has a real-world problem they need to solve. Then, “under the guidance of a faculty member, students spend the semester trying to come up with solutions to that problem,” Auclair says. “It’s all student-driven.”

Hands-on learning opportunities like these give students a competitive advantage when it comes to applying for jobs. “The experiential learning piece [of our program] is what has our students actually stand out above others in the field,” Auclair says, because employers like to see that their candidates are capable of applying their skills in a real-world environment. 

5. Grow your network.

Research shows that 85 percent of all jobs today are filled through networking, making it more important than ever for professionals across industries to invest time and energy into building these vital relationships.

Professionals hoping to establish a career as a research scientist are no exception. These individuals should aim to develop connections with organizations and individuals within the greater biotech industry early on in their careers, and use those relationships to help carve their path forward.

Northeastern’s master’s in biotechnology program has strategically created many great opportunities for students to network throughout their time in the program. They are encouraged to build relationships with their classmates, guest speakers, faculty, and even the industry leaders they meet through co-ops and XN projects. As a result, they establish various impactful connections with individuals at different stages in their careers, all before they graduate.

Learn More: Networking Tips for Scientists

Another way Northeastern’s program supports networking is through opportunities for student/faculty collaboration. “We encourage our students to interact with our own faculty who are research scientists as much as possible, whether that’s volunteering in their lab or finding a half an hour to talk to them about what they’re doing,” Auclair says. “We want our students to be exposed to as many research scientists as possible while they’re in the program.”

Take the Next Step

Pursuing a master’s degree in biotechnology from a top university like Northeastern is a great way for aspiring research scientists to break into the field. Students in these programs can hone related skill sets, grow their professional networks, and experience hands-on learning, all while pursuing graduate-level education. 

Learn more about how a master’s in biotechnology can set you up for success as a research scientist on our program page , then get in touch with our enrollment coaches who can help you take the first step.

Subscribe below to receive future content from the Graduate Programs Blog.

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How To Become A Scientist: A New Scientist Careers Guide

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What does a scientist do?

Scientists are, by definition, individuals who study and gain expertise in one of the natural sciences: biology , chemistry , physics , astronomy and earth sciences . Each of these five main natural sciences has its own subdivisions, and the scope of work will vary for each subdivision. 

As a scientist, you will study and research a topic within one of these fields, often in great detail. Research scientists are essentially experts in specific topics within their fields.

However, even though there are many different types of scientists, the responsibilities of all research scientists are very similar.

Most scientists will need to propose their own research and gain funding for it from relevant organisations. As a scientist, you will therefore need to write up research proposals and funding applications. 

Once you start your research, you may conduct it in a lab, out in the field, or in a specialised facility, depending on the research topic. When you obtain results, you will then need to analyse them and present them to other scientists, as well as write up research papers to publish in journals or books. 

Being a scientist can be a very gratifying job, as you will often help in the development of new products. For instance, you might develop new tests as a biomedical scientist , advance technologies like artificial intelligence (AI) or work with machine learning as a computer scientist, or produce new medical guidelines as a clinical scientist .

You will also support scientists and workers in other disciplines. For example, as a data scientist you will analyse large sets of data to produce insights into datasets that may then be used in further scientific research.

How to become a scientist

Becoming a scientist requires a lot of studying and researching. Y ou wi ll need to complete an undergraduate degree in the field you woul d like to work in. F or example, if you woul d like to become a forensic scientist you should study forensic science or a related subject such as criminology. 

To obtain an undergraduate degree in a specific science, you will often need to have A levels or equivalent in particular subjects. For example, if you want to apply for a degree in biology, you will need A level biology. 

However, if you only decide what you would like to do after your secondary education, some universities offer foundation year degrees, aimed at those who don’t have a relevant A level or equivalent qualification.

After completing a bachelor’s degree, many aspiring scientists choose to do a master’s degree, and many scientists eventually also obtain a doctorate, but how common this is depends on the field.

It is important to gain some hands-on experience during your studies to become a scientist, regardless of the speciality you choose. This is because in addition to knowledge and qualifications, scientists also need to develop skills such as problem-solving, effective communication and teamwork.

Some industries, such as data science or computer science , offer postgraduate job posts for people straight out of university. These are often designed as training posts, where you will complete a training programme rotating between different teams in the company before becoming a permanent employee.

Additionally, some companies offer their employees the opportunity to undertake a fully- or partially-funded masters or even a PhD while working.  

How long does it take to become a scientist?

The exact duration of training depends on the field of study you choose. However, for most scientist jobs , it will take at least three to four years to complete the required undergraduate degree. 

It is common to then take a year to complete a master’s degree, and many scientists also undertake a PhD which can last around three to five years.

So, depending on the route you choose, you could spend between three and 10 years in education. But, for example, if you do a PhD, you will be working independently as a research scientist during that time, and will be paid for your research.  

A day in the life of a scientist

Different scientists will spend their days in different work environments and may carry out a variety of different specific tasks. Nevertheless, the broad responsibilities are often similar for many scientists across different fields of study.

For example, most research scientists will need to do tasks like proposing projects, designing and carrying out experiments in a lab or out in the field, and writing up and presenting the findings.

Other types of scientists, like engineers or computer scientists, will also need to propose projects, but they will focus less on data collection and interpretation, and more on developing new technology .  

Scientist: Career options

S cientist s work around 40 hours a week. Depending on which field they work in and what kind of role they take on , they may need to work some evenings, weekends or bank holidays, and some might even work unsocial hours. For example, research scientists will often need to spend weekends doing administrative work , forensic scientists may be called out on weekends or during the night, and computer scientists may need to work out of hours to finish projects on time.

There are many directions your science career can take. These narrow down as you choose your scientific field and subspeciality.

For example, if you know you want to study life sciences, or more specifically be a scientist in a biology-related field, you could become anything from a molecular biologist , a zoologist or a wildlife biologist to a climate change scientist.

Similarly, if you would like to study physics or maths, you might become an astrophysicist, an applied mathematician , a statistician or even an engineer.

Within most academic-oriented science careers, the natural career progression is from academic research scientist to senior research fellow, and eventually professor. This progression will require an increasing level of independence and you will need to publish original research and lead research teams to become a professor. 

As a scientist, you may also work in a more industrial setting. For example, pharmacologists often work in large pharmaceutical companies, where they help to design and research new medication, or produce already established pharmaceuticals .

Other fields, such as physics, computer science or even medical science, may have less of a research focus as you progress, and more of an applied component. For instance, as a geneticist you might progress from researching molecular genetics to a career in medical genetics and advising on genetic conditions. 

Or, as a computer scientist, you might take on managerial roles as you become more senior and lead a team of junior computer scientists to develop new software or systems.

In summary, a career in science is broad and offers many opportunities, whichever field you choose.  

How much does a scientist earn in the UK and the US?

As a research scientist in the UK, you might earn between £17,688 and £43,000 depending on your level of expertise. However, this will vary between different scientific fields. 

In the US, the salary range for a scientist is large, starting from around $50,700 to $132,100 for the best paid roles. This will, as in the UK, vary depending on your expertise, specialisation and workplace.  

References:

• National Careers Service. Research Scientist. Available from: https://nationalcareers.service.gov.uk/job-profiles/research-scientist 
• Get Educated. How to become a scientist. Available from: https://www.geteducated.com/careers/how-to-become-a-scientist/#/ 
• Career explorer. Comprehensive list of science related careers and degrees. Available from: https://www.careerexplorer.com/careers/scientist/#comprehensive-list-of-science-related-careers-and-degrees 
• Careers Wales. Scientist: How to become. Available from: https://careerswales.gov.wales/job-information/scientist/how-to-become 
• Career explorer. Scientist salary. Available from: https://www.careerexplorer.com/careers/scientist/salary/

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Our definition of a scientist

A scientist is someone who systematically gathers and uses research and evidence, to make hypotheses and test them, to gain and share understanding and knowledge. A scientist can be further defined by: how they go about this, for instance by use of statistics (statisticians) or data (data scientists). what they’re seeking understanding of, for instance the elements in the universe (chemists, geologists etc), or the stars in the sky (astronomers). where they apply their science, for instance in the food industry (food scientist). However all scientists are united by their relentless curiosity and systematic approach to assuaging it.

If you would like to comment on this definition , please contact us .

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Chapter 1 Science and Scientific Research

What is research? Depending on who you ask, you will likely get very different answers to this seemingly innocuous question. Some people will say that they routinely research different online websites to find the best place to buy goods or services they want. Television news channels supposedly conduct research in the form of viewer polls on topics of public interest such as forthcoming elections or government-funded projects. Undergraduate students research the Internet to find the information they need to complete assigned projects or term papers. Graduate students working on research projects for a professor may see research as collecting or analyzing data related to their project. Businesses and consultants research different potential solutions to remedy organizational problems such as a supply chain bottleneck or to identify customer purchase patterns. However, none of the above can be considered “scientific research” unless: (1) it contributes to a body of science, and (2) it follows the scientific method. This chapter will examine what these terms mean.

What is science? To some, science refers to difficult high school or college-level courses such as physics, chemistry, and biology meant only for the brightest students. To others, science is a craft practiced by scientists in white coats using specialized equipment in their laboratories. Etymologically, the word “science” is derived from the Latin word scientia meaning knowledge. Science refers to a systematic and organized body of knowledge in any area of inquiry that is acquired using “the scientific method” (the scientific method is described further below). Science can be grouped into two broad categories: natural science and social science. Natural science is the science of naturally occurring objects or phenomena, such as light, objects, matter, earth, celestial bodies, or the human body. Natural sciences can be further classified into physical sciences, earth sciences, life sciences, and others. Physical sciences consist of disciplines such as physics (the science of physical objects), chemistry (the science of matter), and astronomy (the science of celestial objects). Earth sciences consist of disciplines such as geology (the science of the earth). Life sciences include disciplines such as biology (the science of human bodies) and botany (the science of plants). In contrast, social science is the science of people or collections of people, such as groups, firms, societies, or economies, and their individual or collective behaviors. Social sciences can be classified into disciplines such as psychology (the science of human behaviors), sociology (the science of social groups), and economics (the science of firms, markets, and economies).

The natural sciences are different from the social sciences in several respects. The natural sciences are very precise, accurate, deterministic, and independent of the person m aking the scientific observations. For instance, a scientific experiment in physics, such as measuring the speed of sound through a certain media or the refractive index of water, should always yield the exact same results, irrespective of the time or place of the experiment, or the person conducting the experiment. If two students conducting the same physics experiment obtain two different values of these physical properties, then it generally means that one or both of those students must be in error. However, the same cannot be said for the social sciences, which tend to be less accurate, deterministic, or unambiguous. For instance, if you measure a person’s happiness using a hypothetical instrument, you may find that the same person is more happy or less happy (or sad) on different days and sometimes, at different times on the same day. One’s happiness may vary depending on the news that person received that day or on the events that transpired earlier during that day. Furthermore, there is not a single instrument or metric that can accurately measure a person’s happiness. Hence, one instrument may calibrate a person as being “more happy” while a second instrument may find that the same person is “less happy” at the same instant in time. In other words, there is a high degree of measurement error in the social sciences and there is considerable uncertainty and little agreement on social science policy decisions. For instance, you will not find many disagreements among natural scientists on the speed of light or the speed of the earth around the sun, but you will find numerous disagreements among social scientists on how to solve a social problem such as reduce global terrorism or rescue an economy from a recession. Any student studying the social sciences must be cognizant of and comfortable with handling higher levels of ambiguity, uncertainty, and error that come with such sciences, which merely reflects the high variability of social objects.

Sciences can also be classified based on their purpose. Basic sciences , also called pure sciences, are those that explain the most basic objects and forces, relationships between them, and laws governing them. Examples include physics, mathematics, and biology. Applied sciences , also called practical sciences, are sciences that apply scientific knowledge from basic sciences in a physical environment. For instance, engineering is an applied science that applies the laws of physics and chemistry for practical applications such as building stronger bridges or fuel efficient combustion engines, while medicine is an applied science that applies the laws of biology for solving human ailments. Both basic and applied sciences are required for human development. However, applied sciences cannot stand on their own right, but instead relies on basic sciences for its progress. Of course, the industry and private enterprises tend to focus more on applied sciences given their practical value, while universities study both basic and applied sciences.

Scientific Knowledge

The purpose of science is to create scientific knowledge. Scientific knowledge refers to a generalized body of laws and theories to explain a phenomenon or behavior of interest that are acquired using the scientific method. Laws are observed patterns of phenomena or behaviors, while theories are systematic explanations of the underlying phenomenon or behavior. For instance, in physics, the Newtonian Laws of Motion describe what happens when an object is in a state of rest or motion (Newton’s First Law), what force is needed to move a stationary object or stop a moving object (Newton’s Second Law), and what happens when two objects collide (Newton’s Third Law). Collectively, the three laws constitute the basis of classical mechanics – a theory of moving objects. Likewise, the theory of optics explains the properties of light and how it behaves in different media, electromagnetic theory explains the properties of electricity and how to generate it, quantum mechanics explains the properties of subatomic \particles, and thermodynamics explains the properties of energy and mechanical work. An introductory college level text book in physics will likely contain separate chapters devoted to each of these theories. Similar theories are also available in social sciences. For instance, cognitive dissonance theory in psychology explains how people react when their observations of an event is different from what they expected of that event, general deterrence theory explains why some people engage in improper or criminal behaviors, such as illegally download music or commit software piracy, and the theory of planned behavior explains how people make conscious reasoned choices in their everyday lives.

The goal of scientific research is to discover laws and postulate theories that can explain natural or social phenomena, or in other words, build scientific knowledge. It is important to understand that this knowledge may be imperfect or even quite far from the truth. Sometimes, there may not be a single universal truth, but rather an equilibrium of “multiple truths.” We must understand that the theories, upon which scientific knowledge is based, are only explanations of a particular phenomenon, as suggested by a scientist. As such, there may be good or poor explanations, depending on the extent to which those explanations fit well with reality, and consequently, there may be good or poor theories. The progress of science is marked by our progression over time from poorer theories to better theories, through better observations using more accurate instruments and more informed logical reasoning.

We arrive at scientific laws or theories through a process of logic and evidence. Logic (theory) and evidence (observations) are the two, and only two, pillars upon which scientific knowledge is based. In science, theories and observations are interrelated and cannot exist without each other. Theories provide meaning and significance to what we observe, and observations help validate or refine existing theory or construct new theory. Any other means of knowledge acquisition, such as faith or authority cannot be considered science.

Scientific Research

Given that theories and observations are the two pillars of science, scientific research operates at two levels: a theoretical level and an empirical level. The theoretical level is concerned with developing abstract concepts about a natural or social phenomenon and relationships between those concepts (i.e., build “theories”), while the empirical level is concerned with testing the theoretical concepts and relationships to see how well they reflect our observations of reality, with the goal of ultimately building better theories. Over time, a theory becomes more and more refined (i.e., fits the observed reality better), and the science gains maturity. Scientific research involves continually moving back and forth between theory and observations. Both theory and observations are essential components of scientific research. For instance, relying solely on observations for making inferences and ignoring theory is not considered valid scientific research.

Depending on a researcher’s training and interest, scientific inquiry may take one of two possible forms: inductive or deductive. In inductive research , the goal of a researcher is to infer theoretical concepts and patterns from observed data. In deductive research , the goal of the researcher is to test concepts and patterns known from theory using new empirical data. Hence, inductive research is also called theory-building research, and deductive research is theory-testing research. Note here that the goal of theory-testing is not just to test a theory, but possibly to refine, improve, and extend it. Figure 1.1 depicts the complementary nature of inductive and deductive research. Note that inductive and deductive research are two halves of the research cycle that constantly iterates between theory and observations. You cannot do inductive or deductive research if you are not familiar with both the theory and data components of research. Naturally, a complete researcher is one who can traverse the entire research cycle and can handle both inductive and deductive research.

It is important to understand that theory-building (inductive research) and theory-testing (deductive research) are both critical for the advancement of science. Elegant theories are not valuable if they do not match with reality. Likewise, mountains of data are also useless until they can contribute to the construction to meaningful theories. Rather than viewing these two processes in a circular relationship, as shown in Figure 1.1, perhaps they can be better viewed as a helix, with each iteration between theory and data contributing to better explanations of the phenomenon of interest and better theories. Though both inductive and deductive research are important for the advancement of science, it appears that inductive (theory-building) research is more valuable when there are few prior theories or explanations, while deductive (theory-testing) research is more productive when there are many competing theories of the same phenomenon and researchers are interested in knowing which theory works best and under what circumstances.

Figure 1.1. The Cycle of Research

Theory building and theory testing are particularly difficult in the social sciences, given the imprecise nature of the theoretical concepts, inadequate tools to measure them, and the presence of many unaccounted factors that can also influence the phenomenon of interest. It is also very difficult to refute theories that do not work. For instance, Karl Marx’s theory of communism as an effective means of economic production withstood for decades, before it was finally discredited as being inferior to capitalism in promoting economic growth and social welfare. Erstwhile communist economies like the Soviet Union and China eventually moved toward more capitalistic economies characterized by profit-maximizing private enterprises. However, the recent collapse of the mortgage and financial industries in the United States demonstrates that capitalism also has its flaws and is not as effective in fostering economic growth and social welfare as previously presumed. Unlike theories in the natural sciences, social science theories are rarely perfect, which provides numerous opportunities for researchers to improve those theories or build their own alternative theories.

Conducting scientific research, therefore, requires two sets of skills – theoretical and methodological – needed to operate in the theoretical and empirical levels respectively. Methodological skills (“know-how”) are relatively standard, invariant across disciplines, and easily acquired through doctoral programs. However, theoretical skills (“know-what”) is considerably harder to master, requires years of observation and reflection, and are tacit skills that cannot be “taught” but rather learned though experience. All of the greatest scientists in the history of mankind, such as Galileo, Newton, Einstein, Neils Bohr, Adam Smith, Charles Darwin, and Herbert Simon, were master theoreticians, and they are remembered for the theories they postulated that transformed the course of science. Methodological skills are needed to be an ordinary researcher, but theoretical skills are needed to be an extraordinary researcher!

Scientific Method

In the preceding sections, we described science as knowledge acquired through a scientific method. So what exactly is the “scientific method”? Scientific method refers to a standardized set of techniques for building scientific knowledge, such as how to make valid observations, how to interpret results, and how to generalize those results. The scientific method allows researchers to independently and impartially test preexisting theories and prior findings, and subject them to open debate, modifications, or enhancements. The scientific method must satisfy four characteristics:

• Replicability: Others should be able to independently replicate or repeat a scientific study and obtain similar, if not identical, results.
• Precision: Theoretical concepts, which are often hard to measure, must be defined with such precision that others can use those definitions to measure those concepts and test that theory.
• Falsifiability: A theory must be stated in a way that it can be disproven. Theories that cannot be tested or falsified are not scientific theories and any such knowledge is not scientific knowledge. A theory that is specified in imprecise terms or whose concepts are not accurately measurable cannot be tested, and is therefore not scientific. Sigmund Freud’s ideas on psychoanalysis fall into this category and is therefore not considered a

“theory”, even though psychoanalysis may have practical utility in treating certain types of ailments.

• Parsimony: When there are multiple explanations of a phenomenon, scientists must always accept the simplest or logically most economical explanation. This concept is called parsimony or “Occam’s razor.” Parsimony prevents scientists from pursuing overly complex or outlandish theories with endless number of concepts and relationships that may explain a little bit of everything but nothing in particular.

Any branch of inquiry that does not allow the scientific method to test its basic laws or theories cannot be called “science.” For instance, theology (the study of religion) is not science because theological ideas (such as the presence of God) cannot be tested by independent observers using a replicable, precise, falsifiable, and parsimonious method. Similarly, arts, music, literature, humanities, and law are also not considered science, even though they are creative and worthwhile endeavors in their own right.

The scientific method, as applied to social sciences, includes a variety of research approaches, tools, and techniques, such as qualitative and quantitative data, statistical analysis, experiments, field surveys, case research, and so forth. Most of this book is devoted to learning about these different methods. However, recognize that the scientific method operates primarily at the empirical level of research, i.e., how to make observations and analyze and interpret these observations. Very little of this method is directly pertinent to the theoretical level, which is really the more challenging part of scientific research.

Types of Scientific Research

Depending on the purpose of research, scientific research projects can be grouped into three types: exploratory, descriptive, and explanatory. Exploratory research is often conducted in new areas of inquiry, where the goals of the research are: (1) to scope out the magnitude or extent of a particular phenomenon, problem, or behavior, (2) to generate some initial ideas (or “hunches”) about that phenomenon, or (3) to test the feasibility of undertaking a more extensive study regarding that phenomenon. For instance, if the citizens of a country are generally dissatisfied with governmental policies regarding during an economic recession, exploratory research may be directed at measuring the extent of citizens’ dissatisfaction, understanding how such dissatisfaction is manifested, such as the frequency of public protests, and the presumed causes of such dissatisfaction, such as ineffective government policies in dealing with inflation, interest rates, unemployment, or higher taxes. Such research may include examination of publicly reported figures, such as estimates of economic indicators, such as gross domestic product (GDP), unemployment, and consumer price index, as archived by third-party sources, obtained through interviews of experts, eminent economists, or key government officials, and/or derived from studying historical examples of dealing with similar problems. This research may not lead to a very accurate understanding of the target problem, but may be worthwhile in scoping out the nature and extent of the problem and serve as a useful precursor to more in-depth research.

Descriptive research is directed at making careful observations and detailed documentation of a phenomenon of interest. These observations must be based on the scientific method (i.e., must be replicable, precise, etc.), and therefore, are more reliable than casual observations by untrained people. Examples of descriptive research are tabulation of demographic statistics by the United States Census Bureau or employment statistics by the Bureau of Labor, who use the same or similar instruments for estimating employment by sector or population growth by ethnicity over multiple employment surveys or censuses. If any changes are made to the measuring instruments, estimates are provided with and without the changed instrumentation to allow the readers to make a fair before-and-after comparison regarding population or employment trends. Other descriptive research may include chronicling ethnographic reports of gang activities among adolescent youth in urban populations, the persistence or evolution of religious, cultural, or ethnic practices in select communities, and the role of technologies such as Twitter and instant messaging in the spread of democracy movements in Middle Eastern countries.

Explanatory research seeks explanations of observed phenomena, problems, or behaviors. While descriptive research examines the what, where, and when of a phenomenon, explanatory research seeks answers to why and how types of questions. It attempts to “connect the dots” in research, by identifying causal factors and outcomes of the target phenomenon. Examples include understanding the reasons behind adolescent crime or gang violence, with the goal of prescribing strategies to overcome such societal ailments. Most academic or doctoral research belongs to the explanation category, though some amount of exploratory and/or descriptive research may also be needed during initial phases of academic research. Seeking explanations for observed events requires strong theoretical and interpretation skills, along with intuition, insights, and personal experience. Those who can do it well are also the most prized scientists in their disciplines.

History of Scientific Thought

Before closing this chapter, it may be interesting to go back in history and see how science has evolved over time and identify the key scientific minds in this evolution. Although instances of scientific progress have been documented over many centuries, the terms “science,” “scientists,” and the “scientific method” were coined only in the 19 th century. Prior to this time, science was viewed as a part of philosophy, and coexisted with other branches of philosophy such as logic, metaphysics, ethics, and aesthetics, although the boundaries between some of these branches were blurred.

In the earliest days of human inquiry, knowledge was usually recognized in terms of theological precepts based on faith. This was challenged by Greek philosophers such as Plato, Aristotle, and Socrates during the 3 rd century BC, who suggested that the fundamental nature of being and the world can be understood more accurately through a process of systematic logical reasoning called rationalism . In particular, Aristotle’s classic work Metaphysics (literally meaning “beyond physical [existence]”) separated theology (the study of Gods) from ontology (the study of being and existence) and universal science (the study of first principles, upon which logic is based). Rationalism (not to be confused with “rationality”) views reason as the source of knowledge or justification, and suggests that the criterion of truth is not sensory but rather intellectual and deductive, often derived from a set of first principles or axioms (such as Aristotle’s “law of non-contradiction”).

The next major shift in scientific thought occurred during the 16 th century, when British philosopher Francis Bacon (1561-1626) suggested that knowledge can only be derived from observations in the real world. Based on this premise, Bacon emphasized knowledge acquisition as an empirical activity (rather than as a reasoning activity), and developed empiricism as an influential branch of philosophy. Bacon’s works led to the popularization of inductive methods of scientific inquiry, the development of the “scientific method” (originally called the “Baconian method”), consisting of systematic observation, measurement, and experimentation, and may have even sowed the seeds of atheism or the rejection of theological precepts as “unobservable.”

Empiricism continued to clash with rationalism throughout the Middle Ages, as philosophers sought the most effective way of gaining valid knowledge. French philosopher Rene Descartes sided with the rationalists, while British philosophers John Locke and David Hume sided with the empiricists. Other scientists, such as Galileo Galilei and Sir Issac Newton, attempted to fuse the two ideas into natural philosophy (the philosophy of nature), to focus specifically on understanding nature and the physical universe, which is considered to be the precursor of the natural sciences. Galileo (1564-1642) was perhaps the first to state that the laws of nature are mathematical, and contributed to the field of astronomy through an innovative combination of experimentation and mathematics.

In the 18 th century, German philosopher Immanuel Kant sought to resolve the dispute between empiricism and rationalism in his book Critique of Pure Reason , by arguing that experience is purely subjective and processing them using pure reason without first delving into the subjective nature of experiences will lead to theoretical illusions. Kant’s ideas led to the development of German idealism , which inspired later development of interpretive techniques such as phenomenology, hermeneutics, and critical social theory.

At about the same time, French philosopher Auguste Comte (1798–1857), founder of the discipline of sociology, attempted to blend rationalism and empiricism in a new doctrine called positivism . He suggested that theory and observations have circular dependence on each other. While theories may be created via reasoning, they are only authentic if they can be verified through observations. The emphasis on verification started the separation of modern science from philosophy and metaphysics and further development of the “scientific method” as the primary means of validating scientific claims. Comte’s ideas were expanded by Emile Durkheim in his development of sociological positivism (positivism as a foundation for social research) and Ludwig Wittgenstein in logical positivism.

In the early 20 th century, strong accounts of positivism were rejected by interpretive sociologists (antipositivists) belonging to the German idealism school of thought. Positivism was typically equated with quantitative research methods such as experiments and surveys and without any explicit philosophical commitments, while antipositivism employed qualitative methods such as unstructured interviews and participant observation. Even practitioners of positivism, such as American sociologist Paul Lazarsfield who pioneered large-scale survey research and statistical techniques for analyzing survey data, acknowledged potential problems of observer bias and structural limitations in positivist inquiry. In response, antipositivists emphasized that social actions must be studied though interpretive means based upon an understanding the meaning and purpose that individuals attach to their personal actions, which inspired Georg Simmel’s work on symbolic interactionism, Max Weber’s work on ideal types, and Edmund Husserl’s work on phenomenology.

In the mid-to-late 20 th century, both positivist and antipositivist schools of thought were subjected to criticisms and modifications. British philosopher Sir Karl Popper suggested that human knowledge is based not on unchallengeable, rock solid foundations, but rather on a set of tentative conjectures that can never be proven conclusively, but only disproven. Empirical evidence is the basis for disproving these conjectures or “theories.” This metatheoretical stance, called postpositivism (or postempiricism), amends positivism by suggesting that it is impossible to verify the truth although it is possible to reject false beliefs, though it retains the positivist notion of an objective truth and its emphasis on the scientific method.

Likewise, antipositivists have also been criticized for trying only to understand society but not critiquing and changing society for the better. The roots of this thought lie in Das Capital , written by German philosophers Karl Marx and Friedrich Engels, which critiqued capitalistic societies as being social inequitable and inefficient, and recommended resolving this inequity through class conflict and proletarian revolutions. Marxism inspired social revolutions in countries such as Germany, Italy, Russia, and China, but generally failed to accomplish the social equality that it aspired. Critical research (also called critical theory) propounded by Max Horkheimer and Jurgen Habermas in the 20 th century, retains similar ideas of critiquing and resolving social inequality, and adds that people can and should consciously act to change their social and economic circumstances, although their ability to do so is constrained by various forms of social, cultural and political domination. Critical research attempts to uncover and critique the restrictive and alienating conditions of the status quo by analyzing the oppositions, conflicts and contradictions in contemporary society, and seeks to eliminate the causes of alienation and domination (i.e., emancipate the oppressed class). More on these different research philosophies and approaches will be covered in future chapters of this book.

• Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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• a forensic scientist
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• social scientists
• A couple of decades ago scientists noticed Panama's climate was slowly growing drier .
• There are scientists who say that the results of the research are flawed .
• Afrocentrism
• applicative
• hard science
• historiography
• orientalist
• suicidology
• technically

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What is a research scientist and how to become one

A research scientist conducts scientific experiments and research to discover new knowledge or improve existing theories. They work in various fields such as biology, chemistry, physics, and engineering. Research scientists conduct experiments, analyze data, and interpret findings to develop hypotheses and theories. They collaborate with other scientists to share knowledge and expertise, publish papers and articles, and present their research. Research scientists also contribute to the development of new technologies and applications that can benefit society.

What experience really stands out on Research Scientist resumes?

Distinguished University Professor and Co-chair, Case Western Reserve University

How long does it takes to become a research scientist?

It typically takes 11-15 years to become a research scientist:

• Years 1-4: Obtaining a Bachelor's degree in a relevant field, such as biology, chemistry, or physics.
• Years 5-9: Pursuing a Doctorate degree in a relevant field, such as biochemistry or molecular biology.
• Years 10-12: Accumulating the necessary work experience, typically 2-4 years, in research and development, data analysis, or lab work.
• Years 13-15: Receiving on-site or on-the-job training, typically 1-2 years, in specific research methods or technologies.
• Salary $89,998
• Growth Rate 17%
• Jobs Number 93,013
• Job Satisfaction 3/5
• Complexity Level Advanced
• Most Common Skill Python
• Most Common Degree Bachelor's degree
• Best State California

Research Scientist pros and cons

Opportunity to make significant contributions to scientific knowledge

Potential for high salary and job security

Possibility of travel to conferences and other research institutions

Personal and professional growth and development

Satisfaction of seeing your research translate into real-world applications

Long and irregular work hours, including nights and weekends

High competition for funding and positions

Pressure to publish and maintain productivity

Limited opportunities for upward mobility or promotion within academia

High levels of stress and pressure to meet deadlines and expectations

Research Scientist career paths

A research scientist can progress in their career by becoming a consultant, supervisor, or quality assurance manager. They can also transition into roles like a case manager, nursing director, or director of clinical operations. Additionally, a research scientist can become a scientist or senior scientist, and eventually a research and development manager or director. They can also become a laboratory manager, quality control manager, or quality control director.

Key steps to become a research scientist

Explore research scientist education requirements.

The educational requirements for a research scientist typically involve a high level of education. According to the data, 60.61% of research scientists hold a doctorate degree, while 31.44% have a master's degree. A bachelor's degree is less common, with only 7.95% of research scientists holding this level of education.

Dr. Kimberlee Mix , Provost Distinguished Professor of Biological Sciences at Loyola University New Orleans, advises that "Keep looking for opportunities to grow and learn. Pursuing an advanced degree may help with earning potential, but also consider online courses in bioinformatics and other certificate programs that will give you a competitive edge." This suggests that while a doctorate or master's degree is often necessary, ongoing education and professional development can also be beneficial for research scientists.

Most common research scientist degrees

Bachelor's

Master's

Start to develop specific research scientist skills

Research scientists will benefit from developing research skills in general, specifically learning quantitative and programming skills. They'll also be helped by developing their critical thinking skills. They'll need to be able to analyze data, and they'll need to be able to think about ethical analysis. According to Autumn Mathias Ph.D., LCSW , Associate Professor at Elms College, "The future of decision-making requires a greater need for tools and data availability, whether they enter the public, non-profit, or private sectors. Students would be well served to develop their quantitative and programming skills if they opt to go into a research-oriented role." Badri Roysam D.Sc., Hugh Roy and Lillie Cranz Cullen University Professor and Chair of the Electrical & Computer Engineering Department at the University of Houston, also states that "The fundamentals of the discipline, and critical thinking skills will continue to be important."

Complete relevant research scientist training and internships

Research research scientist duties and responsibilities.

They perform various tasks, including conducting experiments, analyzing data, and collaborating with colleagues. They also develop new methods and technologies, such as machine learning and image processing approaches, and work on projects related to medical devices, software, and drug discovery. Additionally, they manage laboratory equipment, design and implement adaptive controllers, and create analytical methods for carbohydrate and organic chemical analysis. They also conduct statistical analyses and create reports, proposals, and quality assurance procedures. They work on large-scale national and international studies, conduct research on nutritionally-significant biomarkers, and develop applications for nutritional lipids in foods and beverages. They also participate in collaborations with universities and other organizations.

• Manage the development of innovative visualization and concept mapping of contest environment analysis challenges and analyst skill sets.
• Manage sample inventory via in-house laboratory information management system (LIMS) and implement additional systems for sample and chemical organization.
• Used real-time PCR and DNA sequencing to troubleshoot and validate SNP base and gene expression assays.
• Prepare clear technical presentations to NIH department heads in annual seminars.

Prepare your research scientist resume

When your background is strong enough, you can start writing your research scientist resume.

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Apply for research scientist jobs

Now it's time to start searching for a research scientist job. Consider the tips below for a successful job search:

• Browse job boards for relevant postings
• Consult your professional network
• Reach out to companies you're interested in working for directly
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Are you a Research Scientist?

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Average research scientist salary

The average Research Scientist salary in the United States is $89,998 per year or $43 per hour. Research scientist salaries range between $58,000 and $137,000 per year.

What Am I Worth?

How do research scientists rate their job?

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Research Scientist reviews

Exploring more about reseaching field by building knowledge in a certain subject of research and growing the wisdom and knowledge.

The struggle of not breaking a certain research topic.

It's all about getting data, follow up on project, ensuring that jobs are done properly, write reports after a project is done. You travel if the job or project you're handling is out station.

Nothing really, it's just that sometimes getting data can be very difficult

What I like is that,you get to interact with different people from various communities.Relationships are formed in the process

Research Scientist FAQs

Do you need a ph.d. to be a research scientist, how long does it take to become a research scientist, what degree do you need to become a researcher, what does a research scientist do daily, search for research scientist jobs, research scientist jobs by state.

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Updated April 25, 2024

Editorial Staff

The Zippia Research Team has spent countless hours reviewing resumes, job postings, and government data to determine what goes into getting a job in each phase of life. Professional writers and data scientists comprise the Zippia Research Team.

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