Explain a project and justify choices

Take me through the project you've driven that mattered most, from start to finish: which problem and success metric did you set, which alternative approaches did you weigh and discard, and what reasoning led to those calls?

Spell out how you chose and built your features (including the steps you took to keep data leakage out), what you personally owned and decided, how you planned offline versus online validation, which trade-offs you accepted under time or data limits, and — with hindsight — what you would change to raise business impact.

Overview: This Behavioral & Leadership prompt for a Data Scientist probes full-lifecycle project ownership, depth in feature engineering (including awareness of data leakage), how a success metric is defined, validation strategies across offline and online settings, and how decisions get made under constraints, all within the data science and machine learning domain.