Job Description
Rigorous economic reasoning depends on correct application of theory and honest interpretation of data — the same discipline AI systems need to learn before they can be trusted with economic analysis. Chegg is seeking economics professionals to evaluate AI-generated analyses, build authoritative reference answers, and flag flawed reasoning in AI-produced content. Fully remote and asynchronous.
Core Responsibilities
- Evaluate AI-generated responses to microeconomics, macroeconomics, and econometrics questions for accuracy, reasoning quality, and completeness
- Write reference-standard solutions with correct economic models and quantitative reasoning with correct established economic theory and statistical methodology and clear explanations that AI models learn from
- Identify misapplied theory, calculation errors, or unsupported economic claims in AI-produced content, tagging the specific error category
- Compare and rank multiple AI responses to the same problem, explaining which best reflects sound methodology and accurate interpretation
- Test whether AI systems handle ambiguous or data-heavy economic scenarios correctly
- Create realistic synthetic case studies covering market analysis, policy evaluation, and econometrics used to train and evaluate models
- Contribute to rubric design — defining scoring criteria such as accuracy, theoretical grounding, and clarity — used to benchmark AI performance at scale
- Complete asynchronous task batches independently — no scheduled calls or fixed hours required
Key Qualifications
- Master’s degree or PhD in Economics or a closely related quantitative field (or equivalent recognized qualification); relevant experience is a plus — postgraduate and doctoral graduates welcome
- Strong grounding in microeconomic and macroeconomic theory and econometric methods
- Proficiency with statistical or econometric software (R, Stata, EViews) a plus
- Sharp eye for subtle factual, logical, or methodological error — can detect and explain flawed reasoning in plain English
- Self-directed, detail-oriented, and comfortable delivering quality work asynchronously
Nice to Have
- Published research or graduate teaching experience in economics
- Proficiency with Python or R for econometric analysis, and causal inference methods (difference-in-differences, instrumental variables, regression discontinuity)
- Experience in policy analysis, market research, or applied econometrics
- Prior experience in content review, QA, or AI/ML data annotation projects
Why Chegg
- Fully remote and flexible — no fixed schedule
- Task-based commitment, typically 10–40 hours per week
- Real-world challenges with direct impact on AI accuracy and safety
- Pathway to ongoing projects for high-quality contributors