Econometrics Instructor

your Jared; Northern (KY); Hybrid; USD 55,000 - 96,000; Part time

Benefits offered by this job

Flexible schedule

Remote work

Job summary

Alignerr (Powered by Labelbox) is seeking experienced econometrics instructors and quantitative economists to shape AI training datasets and model evaluation. This fully remote, hourly contract role lets you work with top AI labs and contribute real pedagogical rigor.

Review econometric questions, validate models, provide structured feedback, and help AI systems reason about quantitative problems. 10–40 hours per week, flexible schedule, and potential for ongoing work.

Qualifications

• 3+ years of teaching econometrics or related statistical disciplines.
• Strong command of regression analysis, causal inference, hypothesis testing, and statistical modeling.
• Ability to provide clear, actionable written feedback.

Responsibilities

• Review econometric questions, models, and explanations used in AI training datasets.
• Assess statistical accuracy, assumptions, and interpretation of quantitative results.
• Identify errors, edge cases, and misleading reasoning in AI-generated economic analysis.
• Provide clear, structured feedback to improve model reasoning and pedagogical clarity.
• Apply teaching experience to ensure AI explanations meet expert instruction standards.

Skills

Regression analysis

Causal inference

Hypothesis testing

Statistical modeling

Written feedback

Remote work discipline

Education

Master's or PhD in Economics/Statistics

Tools

R

Stata

Python

Job description

About the Role

We're looking for experienced econometrics instructors and quantitative economists to help shape the next generation of AI. Your deep knowledge of statistical methods, causal inference, and economic modeling will directly influence how AI systems reason through complex quantitative problems — making a real and lasting impact on the field.

This is a fully remote, flexible contract role powered by Alignerr (a Labelbox company), working alongside top AI research labs on cutting-edge model development.

• Organization: Alignerr (Powered by Labelbox)
• Type: Hourly / Task-based 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, underlying assumptions, and the interpretation of quantitative results
• Identify errors, edge cases, and misleading reasoning in AI-generated economic analysis
• Provide clear, structured feedback to improve model reasoning, depth, and pedagogical clarity
• Apply your real-world teaching experience to ensure AI explanations meet the standards of expert instruction
• Work independently and asynchronously to complete task-based assignments on your own schedule

Who You Are

• 3+ years of experience teaching econometrics, quantitative economics, or related statistical disciplines
• Strong command of regression analysis, causal inference, hypothesis testing, and statistical modeling
• Able to evaluate and articulate quantitative reasoning with precision and clarity
• Comfortable reviewing structured technical content and providing actionable written feedback
• Self-motivated and reliable when working independently in a remote environment

Nice to Have

• Master's or PhD in Economics, Statistics, or a related quantitative field
• Hands-on experience with statistical tools such as R, Stata, or Python
• Familiarity with AI model evaluation workflows or data annotation platforms
• Background in applied econometrics, policy analysis, or academic research

Why Join Us

• Work on cutting-edge AI projects with leading research labs at the frontier of model development
• Fully remote and flexible — complete tasks on your own schedule, from anywhere
• Freelance perks: full autonomy, variety of tasks, and collaboration with a global expert community
• Contribute to meaningful work that shapes how AI understands and teaches quantitative reasoning
• Potential for ongoing work and contract extension based on performance