IntermediateBEHAVIORAL
Describe a time you identified data leakage in a production ML pipeline. What steps did you take to detect, quantify, and eliminate the leakage, and how did you validate the fix before deployment?
Machine Learning Engineer
General

Sample Answer

Situation: At General Health Analytics, our readmission risk model (XGBoost) suddenly reported AUC rising from 0.78 to 0.95 in production, while clinical teams reported worse-than-expected predictions. Task: I was the ML engineer responsible for model reliability and had to determine whether the improvement was real or caused by leakage and remediate it within two sprints. Action: I ran feature-importance drift checks (SHAP and permutation importance) and temporal feature correlation analyses using Great Expectations and Evidently. I found a derived feature that pulled next-visit billing codes via an eventual-consistency join, creating target leakage. I quantified leakage by retraining on temporally-split data (train up to T, validate T+1) and by masking suspected features: AUC dropped from 0.95 to 0.79 when masked, matching historical baselines. I implemented fixes—replaced the join with streaming-safe lookups, added time-aware unit tests, and a gating job in Airflow that enforces causal feature availability. Result: After rollout to canary traffic, calibration (Brier score) improved by 12% and production AUC stabilized at 0.79; false positive rate reduced 22%, preventing unnecessary interventions estimated to save ~$120K/year.

Keywords

Use of STAR format with concrete timeline and responsibilityMention specific tools: XGBoost, SHAP, Great Expectations, Evidently, AirflowShow quantitative impact: AUC, Brier score, false positive reduction, cost savedExplain detection (feature importance/drift) and quantification (retraining, masking)Describe code/data fixes and CI/CD safeguards (time-aware tests, gating)
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