Econometrics Instructor

Econometrics Instructor (AI Training)
About The Role
We're looking for experienced econometrics instructors and quantitative economists to help shape how AI understands and communicates statistical reasoning. Your expertise will directly influence the quality of AI systems trained on economic analysis — making a real impact on how the next generation of AI handles causal inference, regression, and quantitative methods.
Organization: Alignerr (Powered by Labelbox)
Type: Hourly Contract
Location: Remote
Commitment: 10–40 hours/week
What You'll Do
Review and validate econometric questions, models, and explanations used in AI training datasets
Assess statistical accuracy, model assumptions, and the interpretation of results
Identify errors, misconceptions, and edge cases in AI-generated quantitative reasoning
Provide structured, expert feedback to improve model clarity and correctness
Apply your real-world teaching experience to evaluate how well concepts are explained to learners
Work independently and asynchronously on your own schedule
Who You Are
3+ years of experience teaching econometrics or quantitative economics at a college or professional level
Strong command of regression analysis, causal inference, hypothesis testing, and related statistical methods
Ability to clearly evaluate and articulate quantitative reasoning in written form
Comfortable reviewing structured, technical content at scale
Self-motivated and reliable when working independently
Nice to Have
Master's or PhD in Economics, Statistics, or a related field
Proficiency with statistical tools such as R, Stata, or Python
Prior exposure to AI, machine learning, or model evaluation workflows
Why Join Us
Work on cutting-edge AI projects with top research labs
Fully remote and flexible — work on your own schedule
Freelance perks: autonomy, variety, and global collaboration
Contribute to meaningful work that improves how AI reasons about economics and statistics
Potential for ongoing work and contract extension

Work arrangement
Yes