Before the PhD · Build your research record

How committees see work experience

Not every useful experience is called research. What matters is what you learned, who can speak to it and how you explain it.

2 min read · By Marc Sleiman

Many applicants spend a year or more working before they apply. How a committee reads that experience depends on three things: how much real research the job involved, whether a PhD economist who supervised you can write a detailed letter, and how you explain the experience in your statement of purpose. The third matters more than most applicants realize.

How different jobs tend to be read

Predocs, Federal Reserve research assistants and research organizations

The strongest. This is real research, supervised by researchers whose letters committees know how to read. See predocs and research pathways.

Economic consulting

Respected and data-heavy. Consultants learn to work carefully and quickly with real data. The work is less like academic research, though, and the letter is strongest when it comes from a PhD economist who supervised your analysis.

Industry data science and analytics

This can be a real advantage if you frame it well. Strong programming and data skills transfer directly to research, where much of the work is cleaning, structuring and analyzing data. Say so in your statement, and show that you understand research projects are different from business projects: the questions are open-ended and the timelines are long. That kind of situational awareness gets noticed.

Teaching

Shows communication skills and commitment, but says little about research ability. Pair it with research experience if you can.

Unrelated jobs

Neutral. They will not hurt you. Explain the gap briefly and focus your application on the evidence of research potential you do have.

Framing matters as much as the job

Not having the most prestigious job is one thing. Failing to explain what your job gave you is another. A committee reads your experience through your statement. Two applicants with the same data science job can look very different: one lists tasks, the other explains how years of building data pipelines prepared them to handle large administrative datasets in research, and names the kind of research questions they now want to answer with those skills. The second applicant shows exactly the self-awareness committees look for.

Whatever your experience, ask yourself three questions before you write about it. What did I learn that a researcher needs? Who can speak to it in a letter? And how does it connect to what I want to study?