Econometrics Instructor

Alignerr; Chicago (IL); Remote; USD 55,000 - 90,000; Part time

Job summary

Alignerr (Powered by Labelbox) is seeking an experienced Econometrics Instructor to help shape AI training by evaluating econometric questions, models, and interpretations. You will review statistical methods used in AI training datasets, identify errors, and provide clear feedback to improve model outputs.

The role is fully remote with an hourly contract, allowing you to set your own schedule. Expect 10–40 hours per week, collaborating asynchronously with researchers and students worldwide.

Qualifications

• 3+ years of university-level econometrics or quantitative economics teaching.
• Strong regression, causal inference, and applied statistics skills.
• Ability to explain quantitative reasoning in writing.
• Detail-oriented review of structured technical content.

Responsibilities

• Review econometric questions and models used in AI training datasets.
• Assess statistical accuracy and interpretation of quantitative results.
• Identify errors and edge cases in AI-generated economic reasoning.
• Provide structured feedback to improve model outputs for clarity and correctness.
• Apply teaching experience to evaluate how concepts are communicated.
• Work independently and asynchronously on task-based assignments.

Skills

Teaching econometrics

Regression analysis

Causal inference

Applied statistics

Written communication

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 expertise will directly influence how AI systems reason through statistical models, causal inference, and economic data — making a real and lasting impact on how these tools serve researchers, students, and professionals worldwide.

Econometrics Instructor (AI Training)

We're looking for experienced econometrics instructors and quantitative economists to help shape the next generation of AI. Your expertise will directly influence how AI systems reason through statistical models, causal inference, and economic data — making a real and lasting impact on how these tools serve researchers, students, and professionals worldwide.

This is a fully remote, flexible contract role where you set your own schedule and work on your own terms.

• 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 the statistical accuracy, underlying assumptions, and interpretation of quantitative results
• Identify errors, logical gaps, and edge cases in AI-generated economic reasoning
• Provide clear, structured feedback to improve model outputs for clarity and correctness
• Apply your real-world teaching experience to evaluate how well concepts are communicated to diverse audiences
• Work independently and asynchronously to complete task-based assignments on your own schedule

Who You Are

• 3+ years of experience teaching econometrics or quantitative economics at a college or university level
• Strong command of regression analysis, causal inference, and applied statistical methods
• Able to clearly evaluate and explain quantitative reasoning in written form
• Comfortable reviewing structured, technical content with precision and consistency
• Self-motivated and reliable in an asynchronous remote environment

Nice to Have

• Master's or PhD in Economics, Statistics, or a related quantitative field
• Proficiency in statistical tools such as R, Stata, or Python
• Prior experience with AI evaluation, data annotation, or model training workflows

Why Join Us

• Work on cutting-edge AI projects with top research labs shaping the future of machine intelligence
• Fully remote and flexible — work when and where it suits you
• Freelance perks: autonomy, variety, and collaboration with experts across the globe
• Unique opportunity to see how large language models are trained and evaluated firsthand
• Potential for ongoing work and contract extension based on performance