Zest·Data Scientist·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Zest data scientist interview that leaned hard into ML fundamentals. The main question was a classic overfitting diagnosis scenario presented as a plot, and they wanted you to walk through the whole thing: identification, root causes, how to properly validate, and fixes.

Questions Asked (1)

You're shown a plot where training error keeps dropping but validation and test error plateau then rise, creating a widening gap. What's the most likely issue, what causes it, how do you confirm it without leaking the test set, and what would you actually do to fix it?

Root Cause AnalysisTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This is a four-part question wrapped in one, which I didn't fully appreciate until I was already halfway through answering just the first part.

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AI HintsAI Generated

Suggested Approach

Start by identifying the classic overfitting pattern: training error decreases while validation/test error increases. Then systematically explain the causes, how to diagnose without test leakage, and practical solutions. Emphasize using validation data for diagnosis and regularization techniques to close the gap.

Pro tip: Always keep the test set completely untouched until the final evaluation; use a validation set or cross-validation for all model selection and hyperparameter tuning. This prevents optimistic bias and ensures your reported performance is trustworthy.

1. Identify the issue

Recognize the widening gap between training and validation/test error as a clear sign of overfitting. The model is memorizing training data rather than learning generalizable patterns.

2. Explain the causes

Discuss common causes: model complexity (too many parameters), insufficient training data, noisy features, or lack of regularization. Also consider data leakage or distribution shift if the gap is extreme.

3. Confirm without test leakage

Use the validation set to monitor performance and learning curves. Perform cross-validation on training data to assess variance. Never use the test set for tuning; only for final evaluation.

4. Apply fixes

Implement regularization (L1/L2, dropout), reduce model complexity, gather more data, use data augmentation, or apply early stopping based on validation performance.

5. Validate improvements

After adjustments, re-evaluate on the validation set. Once satisfied, do a final check on the test set to confirm generalization.

Key Points to Mention

Overfitting: model performs well on training but poorly on unseen data.
Bias-variance tradeoff: high variance leads to overfitting.
Regularization techniques: L1/L2, dropout, early stopping.
Cross-validation for reliable performance estimation.
Learning curves to diagnose overfitting vs. underfitting.
Data augmentation or collecting more data to improve generalization.

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