Hiring nowAI economics, labour and social research

AI Economist

Also called Economic Research Lead (AI)

An AI Economist measures what AI does to work, markets and prices. They give the company evidence it can use to set pricing, shape policy conversations and understand how customers actually change their behaviour.

What they do

The work starts with a question such as how a tool changes output, hiring or wages for the people who use it. The economist designs the study, chooses the data, and decides how to separate the effect of AI from everything else going on. They run experiments or build quasi-experimental comparisons, analyse usage and market data, and test pricing and demand. They talk to workers, customers and communities to check the numbers match reality. Then they write it up clearly for executives, policymakers and researchers, and say what the evidence does not show.

What a strong hire delivers

In the first months, a strong hire turns vague claims about AI impact into a small set of measurable questions. They agree the data needed, launch one or two credible studies, and produce a first readable finding that leaders can act on. They also set standards for how the company talks about economic impact, so public claims stand up to scrutiny.

How you can tell they are good

Ask them to walk through a study they designed, end to end. Listen for how they handled confounding, selection and measurement, and what they got wrong. A strong candidate explains results in plain language and is honest about limits. Ask how they would test an impact claim with messy usage data. Be wary of anyone who reaches for a headline number too quickly.

Why it is hard to fill

The title varies widely, so searches by title miss people. The skill mix is uncommon: rigorous causal methods, real familiarity with how AI products are used, and the ability to communicate with non-economists. Most strong candidates sit in academia, central banks or policy teams and are not looking. Many applicants have one strand but not the others. Demand is high and supply is thin.

Where to find them

Look in labour economics, applied microeconomics and development economics, where experimental and causal methods are everyday tools. Others come from sociology, labour research and evaluation science. They work in frontier labs, government analysis units, central banks, large banks, think tanks and university research groups. Some have moved from tech data science into economics. Check their published work and policy writing.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Economist

  1. Tell us about a study where you had to separate a technology's effect from other changes. How did you design it?

    Strong answers name the comparison group, the identifying assumption and the threats to it. They explain what they checked and what they would change. Worry if they describe only correlations or cannot say why their result should be believed.

  2. How would you measure whether our product changes productivity for the people who use it?

    Good answers start by defining productivity and the outcome, then consider randomised rollout or a staggered comparison. They raise self-selection and measurement bias. Worry if they jump straight to surveys or usage counts alone.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are hiring an AI Economist to measure how AI affects work, markets and pricing. You will design studies, work with real product and market data, and give leaders clear evidence to act on. We value rigour and plain writing.

What you will do

  • Design experiments and quasi-experimental studies on AI's effects on work and markets
  • Analyse usage, market and labour data to estimate causal impact
  • Advise on pricing and demand using empirical evidence
  • Engage workers, customers and communities to ground findings in lived experience
  • Write clear reports and briefings for executives, policymakers and researchers
  • Set standards for how we make and test claims about economic impact

What you will bring

  • Demonstrated work using causal inference methods on real, messy data
  • Experience designing experiments or credible natural-experiment studies
  • Strong grounding in economics, labour research or a related social science
  • Clear writing that non-specialists can follow
  • Examples of research that informed a decision or policy
  • Honesty about uncertainty and limits of evidence

Nice to have

  • Experience in public policy or government analysis
  • Familiarity with how AI products are built and used
  • Experience with participatory or community-based research

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Public policy and economicsWorkingAnalyse institutional choices and distributional effects
  • Causal inferenceWorkingSeparate causal claims from predictive associations
  • Experimental designWorkingChoose comparisons that identify credible effects
  • Worker and community participationWorkingInclude affected workers and communities in decisions

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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