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Lyft·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Second round coding interview for a Data Scientist role at Lyft. Just one problem but it was more involved than I expected going in.

Questions Asked (1)

Q1

Implement stratified k-fold cross validation from scratch.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

I knew the concept well enough to explain it but actually coding it up is a different story.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and assumptions, then outline the algorithm step-by-step, and finally implement it in code while explaining key decisions. Emphasize how stratification preserves class distribution and discuss trade-offs like computational cost and handling of edge cases.

Pro tip: Mention that you would use stratification only when class imbalance is significant, and consider using a fixed random seed for reproducibility. Also, discuss how to handle continuous targets by binning them before stratification.

1. Clarify Requirements and Assumptions

Ask about the input format (e.g., pandas DataFrame, numpy arrays), the target variable type (classification or regression), and whether shuffling is required. Confirm that the goal is to maintain the class distribution in each fold.

2. Design the Algorithm

Explain that for each class, you split its indices into k folds, ensuring each fold gets a proportional number of samples. Then combine the folds from each class to form the final k folds.

3. Implement the Splitting Logic

Write code to iterate over classes, shuffle indices within each class, and distribute them round-robin or using array splitting. Handle cases where a class has fewer samples than k by either warning or adjusting.

4. Generate Train/Validation Sets

For each fold, use it as validation and the rest as training. Ensure that the training set also preserves the overall class distribution as much as possible.

5. Test and Validate

Test with a small dataset, check that each fold's class proportions match the original, and discuss potential pitfalls like empty folds or imbalanced splits.

Key Points to Mention

  • Stratification ensures each fold has the same class distribution as the full dataset, which is crucial for imbalanced datasets.
  • Implementation involves grouping indices by class, splitting each group into k folds, and then merging corresponding folds from each class.
  • Handling edge cases: classes with fewer samples than k, continuous targets (binning), and ensuring reproducibility with random seeds.
  • Trade-offs: Stratified K-Fold can be more computationally expensive than standard K-Fold, especially with many classes.
  • Comparison to standard K-Fold and when to use each (e.g., stratified for classification, standard for regression unless binning).
  • Use of libraries like scikit-learn's StratifiedKFold for reference, but emphasize implementing from scratch to demonstrate understanding.

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