Economist – Empirical Research Replication (AI Research Project)

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

• PhD in Economics (required)
• Hands-on experience with causal inference methods — DiD, 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:

To apply, submit your CV. We review every application personally — no automated screening. If your background is a strong fit, you'll receive a direct link to join the project and complete your application.