Amazon·Data Scientist·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Amazon data scientist interview that was basically a deep dive into a single gnarly fraud modeling scenario. One question, four parts, and an hour to convince them you actually know what you're doing with imbalanced data in production. Felt more like a take-home crammed into a live session.

Questions Asked (1)

You've inherited a fraud detection model with severe class imbalance (positives around 2%) and weak validation performance. How would you diagnose whether it's underfitting or overfitting, propose three interventions you could ship in a single day, design a threshold selection procedure based on a specific cost structure, and define the minimum monitoring you'd put in place at deployment?

Root Cause AnalysisTechnical Trade-offsSystem Design

Author's notes

This was a lot to hold in your head at once.

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AI HintsAI Generated

Suggested Approach

Start by diagnosing underfitting vs overfitting using learning curves and validation metrics like PR-AUC, then propose three quick interventions that address data imbalance and model complexity. Next, design a threshold selection procedure that minimizes expected cost based on the given cost structure, and finally outline a minimal monitoring plan covering data drift, model performance, and business metrics.

Pro tip: Emphasize that with severe class imbalance, accuracy is misleading; focus on precision-recall trade-offs and cost-sensitive evaluation. Also, mention that threshold selection should be revisited periodically as costs and data distribution may change.

1. Diagnose underfitting vs overfitting

Plot learning curves (training vs validation performance) and compare PR-AUC, recall, and precision. If both training and validation are poor, it's underfitting; if training is much better, it's overfitting.

2. Propose three one-day interventions

Suggest: 1) Resampling (e.g., SMOTE or random oversampling) or class weighting, 2) Hyperparameter tuning (e.g., tree depth, regularization), 3) Feature engineering or selection to improve signal.

3. Design threshold selection based on cost structure

Define cost matrix (false positive vs false negative costs), compute expected cost across thresholds on validation set, and select threshold minimizing expected cost. Use PR curve to visualize trade-offs.

4. Define minimum monitoring at deployment

Monitor: 1) Data drift (input feature distributions), 2) Model performance (PR-AUC, recall, precision at chosen threshold), 3) Business metrics (fraud loss, false positive rate), and 4) Prediction distribution.

Key Points to Mention

Use precision-recall AUC (PR-AUC) instead of ROC-AUC due to class imbalance.
Learning curves to diagnose bias vs variance.
Cost-sensitive threshold optimization using expected cost minimization.
Interventions: resampling, class weighting, hyperparameter tuning, feature engineering.
Monitoring: data drift, model performance, business metrics, prediction distribution.
Set up alerts for significant deviations and periodic retraining.

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