Job Description
Sound financial analysis depends on rigorous modeling and honest interpretation of numbers — the same discipline AI systems need to learn before they can be trusted with financial reasoning. Chegg is seeking finance professionals to evaluate AI-generated financial analyses, build authoritative reference solutions, and flag flawed reasoning in AI-produced valuation and accounting content. Fully remote and asynchronous.
Core Responsibilities
- Evaluate AI-generated responses to valuation, accounting, and corporate finance questions for accuracy, reasoning quality, and completeness
- Write reference-standard solutions with correct formulas and valuation methodology with correct GAAP, IFRS, or applicable accounting standards and clear explanations that AI models learn from
- Identify calculation errors, misapplied accounting standards, or flawed financial reasoning 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 conclusions
- Test whether AI systems handle ambiguous or high-stakes financial scenarios (e.g., regulatory disclosure, investment-advice boundaries) appropriately
- Create realistic synthetic financial case studies and analysis questions used to train and evaluate models
- Contribute to rubric design — defining scoring criteria such as accuracy, methodology, 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 Finance, Accounting, or a related field (or equivalent recognized qualification); relevant experience is a plus — postgraduate and doctoral graduates welcome
- Strong grounding in financial statement analysis, valuation methods (DCF, comparables), and GAAP or IFRS standards
- Proficient in Excel-based financial modeling; familiarity with Bloomberg, FactSet, or similar platforms 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
- CFA, CPA/ACCA, or an equivalent globally recognized professional certification
- Experience in quantitative finance — Monte Carlo simulation, Black-Scholes/derivatives pricing, or Python (NumPy, pandas, QuantLib) for financial modeling
- Experience in investment banking, equity research, or corporate FP&A
- 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