JP Morgan Chase·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

ML engineer screen at JP Morgan Chase, basically one question about imbalanced data and that was the whole technical portion.

Questions Asked (1)

How do you handle imbalanced datasets in a machine learning context?

Technical Trade-offsAlgorithms & Data Structures

Author's notes

Ran through the usual stuff: resampling, class weights, different evaluation metrics.

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AI HintsAI Generated

Suggested Approach

Start by acknowledging that imbalanced datasets are common in real-world applications like fraud detection, then outline a structured approach: first diagnose the imbalance, then choose appropriate techniques (data-level, algorithm-level, or hybrid), and finally evaluate with the right metrics. Emphasize that the choice depends on the specific problem, business context, and constraints like interpretability and computational resources.

Pro tip: In finance, false negatives often cost more than false positives, so align your solution with the business cost matrix and mention that you'd validate with precision-recall curves and cost-sensitive learning rather than just accuracy.

1. Diagnose the imbalance

Quantify the class distribution and understand the business impact of misclassification. Determine if the imbalance is severe and whether it's intrinsic or due to sampling.

2. Choose a strategy

Select from data-level (resampling), algorithm-level (cost-sensitive), or hybrid approaches based on data size, interpretability needs, and computational budget.

3. Apply techniques

Implement chosen methods: e.g., SMOTE for oversampling, random undersampling, class weights, or ensemble methods like BalancedRandomForest. Consider combining with cross-validation.

4. Evaluate with appropriate metrics

Use precision, recall, F1-score, AUC-PR, and confusion matrix instead of accuracy. Align evaluation with business costs (e.g., cost-sensitive metrics).

5. Iterate and validate

Test multiple approaches, tune hyperparameters, and validate on a hold-out set that preserves the original imbalance. Monitor for overfitting to synthetic samples.

Key Points to Mention

Resampling techniques: SMOTE, ADASYN, random oversampling/undersampling, and their pros/cons
Algorithm-level approaches: class weights, cost-sensitive learning, and threshold moving
Ensemble methods: BalancedRandomForest, EasyEnsemble, RUSBoost
Evaluation metrics: precision-recall curve, F1-score, AUC-PR, and cost-sensitive metrics
Business context: aligning with cost matrix and false negative/positive trade-offs
Potential pitfalls: overfitting from oversampling, information loss from undersampling, and data leakage during resampling

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