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.