What the work actually is

You receive a business or policy question attached to a real dataset and produce the analysis an applied economist would produce on the job: variable construction, an identification strategy, regression or panel specifications, causal inference where the design supports it, and robustness or sensitivity checks that a referee would ask for. The deliverable is not just a coefficient — it is a documented chain of reasoning covering what you assumed, what you tried and discarded, and where the estimate breaks down. Much of this output is used to train and evaluate AI systems on economic reasoning, so the written trace of why you did something matters as much as the result.

What the screen looks for

AfterQuery's screening is credential-anchored and then depth-tested. Expect verification of the master's degree, the 1–3 years of professional work in economics, econometrics, policy analysis, or a comparable quantitative field, and at least one first-author publication or first-author-equivalent output — a working paper, a policy report you led, a thesis chapter, a technical white paper with your name first. From there the questions push on method: which estimator you chose and why, what identifying assumption you were leaning on, what you did when the parallel-trends plot looked wrong. Vague method-name-dropping is the fastest way to fail. TA experience in causal inference, econometrics, or applied ML is a genuine plus because it signals you can explain a method, not only run it.

Logistics

  • Fully remote and fully asynchronous — no standing meetings, no fixed time zone.
  • Projects are offered individually; you accept or decline each one.
  • Expect 10–20 hours per week on projects you take, across an active 2–3 week window.
  • Observed rates run $60–100/hr, project-based, with placement in that band tied to task complexity and demonstrated depth. Rates are as reported, not guaranteed.

Who this fits

Recent economics master's graduates in research assistant, consulting, central bank, or think-tank roles tend to fit well, as do PhD students with applied fieldwork behind them. The common thread is comfort working alone on an ambiguous technical brief and a habit of writing down assumptions before someone asks for them.