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.