Sample Answer
On a churn prediction project, we rolled a Python XGBoost model from offline AUC of 0.86 into a live A/B test and saw almost no uplift in retention – less than 0.5%, where we’d projected 3–4%. That was a red flag. I pulled event logs and compared training vs. production feature distributions in a Python notebook. Two key features had silent schema changes: a boolean flag became tri-state, and a numeric field was being bucketed differently by the upstream service. I documented the findings with plots and a short one-page summary, walked the team through it in our daily standup, and clearly said, “Our live model is effectively different from what we validated.” I then added feature validation checks, tightened our data contracts with engineering, retrained on the corrected pipeline, and re-ran the experiment. The second test delivered a 3.1% churn reduction and we automated the monitoring as part of our ML ops stack.
Keywords
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