Behavioral & Leadership: Describe an End-to-End ML Project You Led

Context: You are interviewing for a Machine Learning Engineer role in a consumer marketplace environment (two-sided platform with buyers and sellers). Provide a concrete, end-to-end example of a project you personally led.

Answer structure (cover all parts clearly and concisely):

1. Business Objective
   • What problem did you target and why now? What constraints or risks mattered?
2. Stakeholders and Roles
   • Product, engineering, data/ML, infra/ops, measurement/analytics, legal/privacy, support/ops.
3. Success Metrics and Guardrails
   • Primary business KPI(s) and target lift; secondary metrics; operational guardrails (latency, cost, reliability). Define time window and attribution.
4. Data and Pipelines
   • Sources (events, catalog, user profiles), label definition, sampling/propensity, feature store, batch/stream, orchestration, data quality checks.
5. Modeling Choices
   • Baselines; candidate generation vs ranking; algorithms and why; key features; bias/leakage mitigation; cold-start strategy.
6. Training Setup
   • Splits (time-based), hyperparameter search, hardware/scale, frequency, regularization, class imbalance, calibration.
7. Evaluation Methodology
   • Offline metrics and why; counterfactual adjustments (e.g., IPS) if needed; online experiment design (A/A, A/B, power), guardrails, risk mitigation.
8. Infra and Serving
   • Architecture, latency budget, caching, model registry/CI-CD, canary/rollback, monitoring (data/feature drift, performance), alerting.
9. Trade-offs, Failures, and Debugging
   • Key decisions and their trade-offs; what broke, how you diagnosed, what you fixed.
10. Impact and What You’d Do Differently

• Quantified business/ops impact; learnings and next steps for greater impact.