Job Description
Sound operations decisions rest on quantitative rigor — optimization modeling, simulation, and data-driven forecasting — not just process frameworks. Chegg is seeking operations professionals to evaluate AI-generated analyses on supply chain design, capacity planning, and quantitative decision-making, build authoritative reference solutions, and flag flawed operational reasoning. Fully remote and asynchronous.
Core Responsibilities
- Evaluate AI-generated responses to operations questions spanning linear/mixed-integer optimization, discrete-event simulation, inventory and network design, and demand forecasting for accuracy, reasoning quality, and completeness
- Write reference-standard solutions with correct optimization formulations, simulation logic, and quantitative derivations that AI models learn from
- Identify misapplied optimization models, incorrect forecasting methodology, flawed simulation assumptions, or unsupported operational claims in AI-produced content, tagging the specific error category
- Compare and rank multiple AI responses to the same operations problem, explaining which best reflects sound quantitative methodology and practical feasibility
- Test whether AI systems handle ambiguous or data-heavy operations scenarios correctly — including multi-echelon network trade-offs and capacity-constrained scheduling
- Create realistic synthetic case studies covering supply chain network design, production scheduling, and inventory optimization used to train and evaluate models
- Contribute to rubric design — defining scoring criteria such as quantitative accuracy, methodological soundness, and practical applicability — used to benchmark AI performance at scale
- Complete asynchronous task batches independently — no scheduled calls or fixed hours required
Key Qualifications
- MBA with a concentration in Operations or Supply Chain Management, MS in Operations Research, Industrial Engineering, or Supply Chain Management, or PhD in Operations Research, Industrial Engineering, or Management Science (or equivalent recognized qualification); relevant experience is a plus — postgraduate and doctoral graduates welcome
- Strong grounding in optimization modeling (linear/mixed-integer programming), discrete-event simulation, and quantitative forecasting methods
- Practical experience formulating and solving real operations problems — network design, capacity planning, inventory optimization, or production scheduling
- Sharp eye for subtle methodological error — can detect and explain flawed quantitative reasoning in plain English
- Self-directed, detail-oriented, and comfortable delivering quality work asynchronously
Nice to Have
- Hands-on experience with optimization solvers such as Gurobi, CPLEX, or open-source alternatives (Python PuLP, OR-Tools)
- Proficiency with discrete-event simulation platforms such as Arena, AnyLogic, or Simio
- Experience with advanced planning modules such as SAP IBP/APO or Oracle SCM Cloud, beyond generic ERP usage
- Six Sigma Black Belt, PMP, or APICS/ASCM (CPIM, CSCP) certification
- Background in demand-sensing or advanced statistical forecasting (ARIMA, exponential smoothing)
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