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Amazon·Product Manager·Onsite - Product Sense / Strategy·Senior

Senior
Apr 2026

Summary

PM interview at Amazon with a single system design question focused on trust and safety. Pretty open-ended, which I wasn't fully prepared for.

Questions Asked (1)

Q1

How would you design a system to detect and track review abuse on Amazon?

System DesignProduct Analytics & MetricsProduct Strategy
Author's notes

I spent the first few minutes just trying to scope the problem because 'review abuse' covers a lot of ground.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem scope and defining what constitutes review abuse, then outline a multi-layered detection system combining machine learning, rules, and human review. Structure your answer around detection, enforcement, and measurement, emphasizing trade-offs and iteration based on metrics.

Pro tip: Frame your answer around Amazon's leadership principles, such as Customer Obsession and Bias for Action, and highlight how you would balance false positives (hurting legitimate reviewers) with false negatives (allowing abuse).

1. Define Abuse and Objectives

Clarify what types of review abuse exist (fake reviews, incentivized reviews, review bombing) and define success metrics such as precision, recall, and impact on customer trust.

2. Identify Signals and Data Sources

List relevant signals: reviewer behavior (velocity, IP, device), review content (language, sentiment), product context (rating distribution), and network patterns (review rings).

3. Design Detection Mechanisms

Propose a layered approach: rule-based filters for known patterns, ML models (supervised and unsupervised) for anomaly detection, and graph analysis for coordinated behavior.

4. Enforcement and Feedback Loops

Decide on actions (remove review, ban user, suppress ratings) and establish a human review process for edge cases, with feedback to improve models.

5. Measure and Iterate

Define metrics to evaluate system performance (e.g., abuse detection rate, false positive rate, time to detect) and plan for continuous monitoring and model retraining.

Key Points to Mention

  • Use of machine learning models (e.g., supervised classification, anomaly detection) to identify suspicious patterns.
  • Importance of graph-based analysis to detect coordinated review rings and fake accounts.
  • Balancing precision and recall to minimize false positives that could harm legitimate reviewers.
  • Incorporating human-in-the-loop review for ambiguous cases and to generate training data.
  • Measuring business impact: customer trust, conversion rates, and seller fairness.
  • Iterative improvement: A/B testing, model retraining, and adapting to new abuse tactics.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.