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.