Lead a 10–12 minute, interviewer‑driven walkthrough of a recent data challenge you completed. Cover: problem definition and success metric aligned to business impact; data provenance, schema, row/feature counts, missingness, and leakage risks; EDA highlights that materially influenced your approach; baseline(s) and why they were chosen; modeling choices (including a non‑chosen alternative) and hyperparameter strategy; validation design (e.g., time‑based CV if temporal, nested CV if tuning), with rationale; primary and secondary metrics, confidence intervals, and practical significance; ablation and error analysis (show at least two concrete failure modes and fixes); fairness/robustness checks; how you would productionize (data contracts, monitoring, retraining, rollback); and code quality (tests, reproducibility, environment). Expect probing follow‑ups quantifying trade‑offs (e.g., cost per false positive, latency budgets) and defending assumptions under noisy requirements.