Present and defend your data challenge end-to-end

Present a 10–12 minute, interviewer-led run-through of a recent data challenge you completed. Cover: how you defined the problem and chose a success metric tied to business impact; where the data came from, its schema, row and feature counts, missingness, and leakage risks; EDA findings that meaningfully shaped your approach; the baseline or baselines and why you picked them; modeling decisions (including an alternative you did not select) and how you tuned hyperparameters; validation design (for example, time-based CV for temporal data, nested CV when tuning), with the reasoning behind it; primary and secondary metrics, confidence intervals, and practical significance; ablation and error analysis (show at least two concrete failure modes and how you fixed them); fairness and robustness checks; how you would productionize (data contracts, monitoring, retraining, rollback); and code quality (tests, reproducibility, environment). Anticipate probing follow-up questions that ask you to quantify trade-offs (such as cost per false positive or latency budgets) and to defend your assumptions when requirements are unclear.

Overview: This question assesses end-to-end data science and machine learning ability, covering problem definition, data provenance and schema, exploratory data analysis, baselines and modeling choices, validation and metrics, error analysis, fairness and robustness, productionization, and reproducibility.