10–12 Minute Interviewer-Driven Walkthrough: Recent Data Challenge

Provide a concise, structured walkthrough of a real project you led end-to-end. Assume an audience of data scientists and product stakeholders.

Cover the following:

1. Problem Definition and Success Criteria
   • What business problem were you solving?
   • Define the objective function and success metrics in business terms.
2. Data Provenance and Schema
   • Sources (internal/external), row counts, feature counts.
   • Key tables, join keys, and sampling window.
   • Missingness patterns and handling.
   • Leakage risks and mitigations.
3. EDA Highlights
   • Findings that materially influenced approach (e.g., class imbalance, drift, seasonality, segmentation).
4. Baselines
   • Heuristic or simple model baselines and why they’re appropriate.
5. Modeling Choices
   • Chosen model(s), rationale, and hyperparameter tuning strategy.
   • One serious alternative you considered and why you didn’t choose it.
6. Validation Design
   • CV strategy (time-based if temporal, nested if tuning) and rationale.
7. Metrics, Confidence Intervals, and Practical Significance
   • Primary and secondary metrics; show CIs and explain practical impact.
8. Ablation and Error Analysis
   • At least two failure modes you discovered and how you addressed them.
9. Fairness and Robustness Checks
   • Bias assessments and stress tests.
10. Productionization Plan

• Data contracts, monitoring, retraining, and rollback strategy.

11. Code Quality and Reproducibility

• Tests, experiment tracking, environment, and documentation.

12. Trade-off Defenses

• Be ready to quantify costs (e.g., cost per false positive), latency budgets, and defend assumptions under ambiguity.