Job Description
Rigorous statistical inference at the research level — beyond textbook hypothesis testing — is one of the harder problems for AI systems to get right. Chegg is seeking statisticians and biostatisticians to evaluate AI-generated statistical models and derivations, build authoritative reference solutions, and expose precisely where AI statistical reasoning breaks down. This is advanced, model-evaluation-focused work spanning classical and modern statistical methods — fully remote, asynchronous, and highly specialized.
Core Responsibilities
- Evaluate AI-generated statistical analyses spanning survival analysis, Bayesian inference, multivariate methods, and time-series modeling for rigor, correctness, and methodological soundness
- Write publication-quality reference solutions and derivations with correct model specification, assumptions, and interpretation that AI models learn from
- Identify model misspecification, invalid distributional assumptions, misapplied statistical tests, or flawed causal claims in AI-generated content, tagging the specific error category
- Compare and rank multiple AI-generated statistical analyses of the same problem, explaining which reflects superior methodological rigor and correct inference
- Test whether AI systems correctly distinguish between correlation and causation, and appropriately apply causal inference techniques (difference-in-differences, instrumental variables, regression discontinuity) where relevant
- Construct advanced problem sets and datasets that probe AI reasoning on model selection, assumption-checking, and inference under real-world data constraints
- Contribute to rubric design — defining scoring criteria such as methodological rigor, correct model specification, and interpretive accuracy — used to benchmark AI statistical reasoning at scale
- Complete asynchronous task batches independently — no scheduled calls or fixed hours required
Key Qualifications
- Master’s degree or PhD in Statistics, Biostatistics, or a closely related quantitative field (or equivalent recognized qualification); relevant experience is a plus — postgraduate and doctoral graduates welcome
- Strong command of advanced statistical theory — survival analysis, Bayesian inference, multivariate methods, or time-series analysis
- Able to specify, fit, and critique statistical models with correct assumptions and rigorous interpretation of results
- Sharp eye for subtle methodological error — can detect and explain model misspecification or flawed statistical reasoning in plain English
- Self-directed, detail-oriented, and comfortable delivering quality work asynchronously
Nice to Have
- Proficiency with R (survival, lme4, brms/rstan) for survival analysis, mixed-effects modeling, or Bayesian inference
- Proficiency with Python (statsmodels, PyMC, scikit-learn) for computational and machine-learning-adjacent statistical work
- Experience with time-series methods (ARIMA, GARCH) or causal inference techniques
- Published research or graduate teaching experience in statistics or biostatistics
- 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