Design an end-to-end spam detection system
Company: Amazon
Role: Data Scientist
Category: Machine Learning
Difficulty: hard
Interview Round: Technical Screen
Design an end-to-end email spam detection system. Requirements: real-time scoring with p99 latency <50 ms; minimize false positives (target precision ≥98% on hard blocks) while keeping recall high; adversaries evolve tactics. Describe: 1) Problem framing and labeling (ham vs spam; graymail; handling noisy/weak labels and delayed abuse reports). 2) Features and representations (character/word n-grams, sender/domain/IP reputation, URL features, MIME structure, lightweight embeddings), and how you’d prevent leakage (e.g., future knowledge, reply/forward chains). 3) Model choice and serving (e.g., logistic regression vs gradient boosting vs compact transformer), calibration, and thresholding for different enforcement actions (block, quarantine, tag). 4) Training pipeline, sampling to handle prevalence, and drift detection (population/stability metrics, canaries). 5) Offline metrics (PR-AUC, calibrated precision/recall at business thresholds), and online evaluation (A/B design, guardrails, holdouts). 6) Feedback loops and safety (appeals workflow, human-in-the-loop review, bias/privacy/PII handling). 7) Cost, reliability, and rollback plans. Finally, list the top three failure modes you anticipate and concrete mitigations for each.
Quick Answer: This question evaluates a data scientist's system-design and applied machine learning engineering skills—covering problem framing and labeling, feature representation, model selection and calibration, real-time serving constraints, drift detection, and feedback/safety mechanisms—and is commonly asked to probe trade-offs between latency, precision/recall, and robustness against adversarial evolution in production spam detection. Category: Machine Learning; it tests machine learning systems and production-ML competencies at both conceptual-design and practical-application levels, emphasizing calibration, evaluation (offline and online), operational reliability, and rollback/mitigation planning.
