About the Project
Pareto. AI is a human data-collection platform connecting leading AI researchers with trusted industry experts to collaborate on AI alignment, safety, and training projects. We are partnering with a frontier AI lab to evaluate an AI model's ability to replicate empirical economics research findings.
What You'll Do
Identify suitable causal economics papers with publicly available replication data
Write prompts asking the AI model to replicate findings given a research question, dataset, codebook, and context
Write rubrics to evaluate the AI model's performance across each step of the empirical pipeline: Data cleaning, Variable construction, Specification choice, Robustness judgment
Who We're Looking For
Ph D in Economics (required)
Hands-on experience with causal inference methods — Di D, IV, RDD, RCT, natural experiments
Familiarity with replication-friendly microdata — NLSY, ACS, CPS, administrative data
Proficient in STATA, R, or Python
Strong understanding of empirical research workflow from raw data to published results
Bonus: experience with AI/ML tools or interest in AI evaluation
Ideal Background
Active or former academic economist at a research university
Published or working papers in applied microeconomics
Fields: labor, health, development, public, environmental economics
Why Join
Contribute to cutting-edge AI safety and alignment research
Flexible part-time remote work — task-based engagement
Collaborate with a global network of economists and AI researchers
Competitive compensation per completed task
Compensation - $100/hr USD
Apply:
