So You Wanna be a Researcher? Advice for Grad Students

William Marcellino, PhD
7 min readMar 8, 2021
Photo by Edu Lauton on Unsplash

I have the good fortune to teach graduate students at some fantastic universities: Carnegie Mellon University (Institute for Politics and Strategy), Johns Hopkins (Global Securities Studies), as well as PhD students at RAND’s Pardee Graduate school (the first US public policy doctoral program). I’m grateful to teach some of the brightest and most engaged students in the world, but I’ve noticed that even these students often have concerns over their professional future. One of the more high-stakes questions I get from graduate students is “What should I do right now to be employable when I finish my program?” This question is less acute for students in areas that have clear career paths to industry, for example data science. If you are learning how to wrangle data, build machine learning models, or develop business intelligence applications, you likely have a good idea of possible landing spots. But for students in the social sciences, or physical scientists who want to do applied research work such as public policy, the road isn’t quite as well marked.

This is a critical question for those students who want to work outside the Academy and find jobs in the “non-academic” job market. Students really do need to think early about how to best position themselves for employment. Partly they should do this because for many programs, employment considerations are an add-on, often very late in the course of their studies. Another reason though is that the educational outcomes of a graduate program may directly translate to academic applications, but indirectly to broader employment applications.

For example, ethnographic research methods such as participant-observation and key informant interviews are valuable and directly relevant to commercial marketing and product design. However, not every employer will understand this and couch their hiring search using the academic language students learn to swim in during their graduate studies — there’s translation work involved. Further, students may need to show that their interests and skills are relevant to real world problems. In the academic world it is implicit that any location for study has inherent value and expands human knowledge. However, employers outside the Academy (for example in public policy research or in the commercial world) will want to know that prospective hires will be a good fit and have interest in larger shared problems — having done research that seems insularly personal may look like “me-search”

My best answer to the question “What do I do as a grad student to be most employable?” is three specific pieces of advice: acquire expert knowledge in a subject area, gain skills (including research methods), and develop research communications skills. When you leave grad school, chances are you will not have a demonstrated history documenting your value as a researcher. Since employers are generally trying to fill roster slots (something like “We need a good shortstop and left fielder to round out our team”) newer job-seekers can instead show capability in the above areas, supported by credible and specific evidence. I’ll lay out some general principles to consider in the rest of this post, but bear in mind that I am primarily speaking from a public policy research perspective. However, I hope this advice has broader applicability to research employment in the commercial world as well.

Domain Knowledge

Expertise in a specific domain area can be valuable to potential employers, relative to their purposes. For example in national security research, for the first two decades of the 20th century Arab culture experts, Arabic linguists, and Middle East/North Africa area specialists were in high demand. More recently, US attention has shifted away from the region, and a new need for specialists in domestic terror and right-wing extremism has burgeoned. Having domain knowledge that is in demand can help position new grads for employment, but the above example also shows some of the limits of having speciality knowledge. Being a specialist is great when your specialty is in high demand, but the market for domain knowledge can (and will) shift. Russia expertise is a good example: during the Cold War period Russia studies were highly relevant, but after the fall of the Soviet Union much less so…until the age of Putin, where once again Russian domain knowledge is relevant and in demand. So whether it’s China-relevant domain expertise, early childhood development, or community health, showing employers you bring expert knowledge to the table is valuable.


Skills development is also important, and because skills can be applied to many domains, having a robust skillset means more options across the course of a career — if you can do regression analysis or conduct qualitative case study research, that skill will be portable across many research domains. There are multiple ways to gain and document skills, and the clearest path is coursework. As you go beyond your core required courses, look for methods-centric courses. Independent studies are a great location to learn and apply skills to something you find relevant and interesting. Another possibility is on the job training or apprenticing as a research assistant: learning skills from a mentor is as valid as a formal course, and offers the trainee a chance to point to the research project as evidence of their learning. Finally, self-study is also a good way to learn skills: reading foundational and applied literature or taking a self-study course are legitimate ways to acquire skills. However, in the case of self-study it is important to demonstrate/document competence in some way, going beyond reading about a skill to also applying skills to a specific problem or activity (ideally one that results in a project or paper that clearly documents competency). Research methods — that is, research activities such as collecting or analyzing data — are a somewhat special case. Research methods in particular must be deployed within a larger research methodology (a coherent, rigorous design for investigation), and developing research design skills are a particular skill on their own.

Stand Out in Your Skills

I also recommend that students go outside their disciplinary practice area to acquire complementary skills. For example most anthropology students will gain a variety of ethnographic skills: interviewing, participant-observation, and qualitative content analysis methods. The anthropology student who also learns network analysis skills and can build out ego-nets to study actors and their contacts is even better positioned to do meaningful work and get hired. Similarly, many social science students will learn some kind of qualitative content collection and analysis methods, but that same pool is less likely to also be good at web-scraping or computational text-mining methods to conduct mixed method analysis at scale. This runs both ways — it’s great to have natural language processing skills if you are a computer science student, but if you also have chops in collecting and analyzing real world discourse captured in field work, you will bring something special to the table as a potential hire. However you choose to expand your toolkit, do it. Also bear in mind we are in age of increasingly large datasets and scalable analytics, so make sure somewhere you cover data: scalable data collection, scalable data analysis methods, or data visualization. This will help you stand out.

Research Communication Skill

My last piece of advice is learn to communicate research findings to broader audiences. This can be a tricky thing, because you will also need to signal within your expert community that you are a credible member. I remember in my hiring job talk at RAND having to thread that needle: I had to sound like a junior scientist whose work was rigorous and credible, but I also had to show I could explain the purpose, need, and practical take-aways of my research without using technical language. Outside the Academy, you will have to communicate with decision makers such as policy-makers and executives, in language any smart person can understand.

Write for Your Audience, not Your Field

Part of getting good at research communications is recognizing the genre and disciplinary writing norms that are baked into most graduate programs. Sociologists or computational linguists need to learn the insider technical language of their field, but they also need to be able to drop those conventions and use generalist language when communicating with general audiences. When I was exploring research employment, and even after I was hired at RAND, I advertised my skills in discourse analysis. It turns out that very few people outside of linguistics have any idea what discourse analysis is, but they do know what qualitative data analysis is. You also need learn to clearly communicate outside your discipline.

You also need to understand and switch between discourse conventions: the broad language patterns any community adopts. For example in the sciences, most research communication follows a “how I got there” discourse structure: an introduction with a research question, then a data and methods section, followed by reporting findings/results, then a discussion of the takeaways and insights from the work, and finally a summary section. Communication aimed at a policy-maker usually follows the opposite discourse pattern: a bottomline-up-front executive summary that can be read as a stand alone report of what the research means from a practical standout, followed by an unpacking of process that may be read by more junior personnel (method and data explanations may even be in appendixes or separate annexes).

Consider taking a course in technical and professional writing where you learn to think about documents (papers, briefs) as audience-centered tools for someone else to use productively. Learn how and when to use tables to communicate complex numerical data, figures to share complex concepts, and data visualizations to illustrate complex relationships in data.

Get Ready Now

In summary, I recommend taking some time now to deliberately put yourself in potential employer’s shoes. Adopt their perspective: are you someone who has immediate value to a team (you won’t be doing solo work), ready to work on hard problems? Do things now so that after you graduate, the answer is yes.