Hiring nowAI economics, labour and social research

AI Labour Economist

Also called AI Workforce Economist

An AI Labour Economist measures how AI adoption changes work and employment, and says how sure we can be. Companies hire them to ground product, policy and public claims in evidence rather than anecdote.

What they do

The work starts with a question, such as whether an AI tool changes hiring, task mix or earnings in a set of occupations. The economist chooses a design, then gathers data from surveys, administrative or job-posting records, and interviews with workers and managers. They break occupations into tasks and map where AI touches them. They run the analysis with econometric tools, test whether results survive other assumptions, and write up what the data can and cannot show. They also brief policy, product and communications colleagues in plain language.

What a strong hire delivers

In the first months a strong hire agrees a short list of questions the company actually needs answered. They deliver one empirical study with its data sources, method and identification limits stated clearly. They set up a repeatable task framework so later work builds on the first. Colleagues understand what the findings support, what they do not, and which claims to avoid.

How you can tell they are good

Ask them to walk through a past study from question to conclusion. Listen for how they chose the design, what threatened causal claims, and what they did about it. Give them a vague claim about AI and jobs and ask how they would test it. Strong people name limits unprompted and separate causal findings from descriptive ones.

Why it is hard to fill

Few people combine labour economics rigour with real fluency in how AI is used at work. Titles vary widely: workforce economist, labour researcher, policy analyst, evaluation scientist. Many strong candidates sit in academia or public bodies and have never worked in a company. Others know the technology but cannot defend a causal claim. Expect to assess method and domain knowledge separately.

Where to find them

Look in university economics departments, think tanks, central banks and government research units. Labour sociologists, evaluation scientists and survey researchers also move into this work. Some come from research arms of model providers, consultancies or workforce data firms. Titles include labour economist, applied microeconomist, research scientist in economics and policy researcher. Postdocs and senior analysts with field experience are worth a conversation.

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 Labour Economist

  1. Describe a study where you measured employment or task change from a new technology. What was the design and what limited it?

    Covers question, data, design and the main threats to identification, such as selection or confounding. Names what the study could not claim. Worry if they only describe results, or overstate causal certainty.

  2. How would you break an occupation into tasks to judge exposure to AI, and where does that approach mislead?

    Explains task decomposition, using sources like job descriptions, surveys and worker interviews. Notes that exposure is not displacement, and that tasks bundle differently in practice. Worry if they treat exposure scores as outcomes.

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

Job description you can post

We are hiring an AI Labour Economist to measure how AI adoption changes work and employment. You will design and run empirical studies, state clearly what they show and where they fall short, and help us make decisions and public statements that rest on evidence.

What you will do

  • Define research questions on AI adoption, tasks, employment and worker outcomes
  • Design studies using causal or descriptive methods suited to the data available
  • Collect and analyse survey, administrative and interview data
  • Map occupations into tasks and assess where AI changes them
  • Document identification limits and uncertainty in every study
  • Brief product, policy and leadership colleagues in plain language
  • Involve workers and communities in shaping and interpreting research

What you will bring

  • Published or shared empirical work on labour markets, technology or related topics
  • Demonstrated use of causal inference methods and honest treatment of their limits
  • Experience with econometric tools and survey or interview research
  • Evidence of task-level analysis of occupations
  • Clear writing for non-specialist readers
  • Judgement about what data can and cannot support

Nice to have

  • Experience designing experiments or field evaluations
  • Work with public policy bodies or research institutions
  • Familiarity with participatory research methods

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

Skills that matter

  • Public policy and economicsAdvancedAnalyse institutional choices and distributional effects
  • Causal inferenceAdvancedSeparate 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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