About the Project
¿Tiene las cualificaciones y habilidades adecuadas para este trabajo? Descúbralo a continuación y pulse en "solicitar" para ser considerado.
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. xhfqzwm If your background is a strong fit, you'll receive a direct link to join the project and complete your application.