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
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