Explain a project and justify choices
Overview
This Behavioral & Leadership question for a Data Scientist evaluates end-to-end project ownership, technical depth in feature engineering (including data leakage awareness), metric definition, model validation strategies (offline vs. online), and decision-making under constraints within the data science and machine learning domain.
Walk me through your most impactful project end-to-end: what problem and success metric did you define, what alternatives did you evaluate and reject, and why? Detail how you selected and engineered features (including how you avoided data leakage), your role and key decisions, offline vs. online validation plan, trade-offs you made under time or data constraints, and—knowing what you know now—what you would do differently to improve business impact.