Design and critique an abuse-detection ML system
Company: Google
Role: Data Scientist
Category: Machine Learning
Difficulty: hard
Interview Round: Onsite
Overview: This question evaluates system-design and production machine learning competencies including large-scale classification versus risk scoring, handling extreme class imbalance and delayed labels, calibration and thresholding under a fixed human-review budget, near-real-time feature engineering, robustness and drift detection, and privacy and fairness trade-offs. It is commonly asked in the Machine Learning domain for Data Scientist roles to test an interviewee's ability to balance statistical objectives, operational constraints and ethical considerations; the category tested is Machine Learning (Trust & Safety) and the level of abstraction spans both conceptual understanding and practical application in production systems, English summary.
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