CVS Health·Data Scientist·Technical Phone Screen·Senior

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Jun 2026Remote

Summary

CVS Health data scientist interview, technical round, one big coding question that was essentially a full ML pipeline build. Pretty involved for a single prompt but it covered a lot of ground at once.

Questions Asked (1)

Build a reproducible sklearn classification pipeline in Python to predict whether a user will subscribe in the next 30 days. The dataset has user and behavioral features including country, device type, session counts, and purchase history. Requirements include: temporal train/validation split, a ColumnTransformer with separate numeric and categorical preprocessing pipelines, logistic regression with class balancing and hyperparameter search over C using stratified cross-validation, ROC AUC and PR AUC evaluation plus threshold tuning for F1, probability calibration on training data only, permutation feature importance on the validation set, and an explanation of one target leakage risk in the schema.

System DesignTechnical Trade-offsProduct Analytics & Metrics

Author's notes

This was a lot.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a walkthrough of the pipeline, emphasizing reproducibility, correct temporal ordering, and leakage prevention. Explain each component's purpose and how they fit together, and justify key design choices like class balancing and calibration. Conclude by discussing evaluation metrics and the leakage risk.

Pro tip: Mention that you set random_state for all stochastic components and use a Pipeline to encapsulate all preprocessing to prevent data leakage during cross-validation. Also, note that probability calibration should be done on a separate calibration set or via cross-validation to avoid overfitting.

1. Data splitting and temporal validation

Split the data temporally: use earlier data for training and later data for validation to mimic real-world deployment. Ensure no future data leaks into training.

2. Preprocessing with ColumnTransformer

Create separate pipelines for numeric and categorical features: impute missing values, scale numeric features, and one-hot encode categorical features. Wrap in a ColumnTransformer for seamless integration.

3. Model training and hyperparameter tuning

Use logistic regression with class_weight='balanced' and tune C via GridSearchCV or RandomizedSearchCV with stratified cross-validation on the training set. Optimize for ROC AUC or PR AUC.

4. Evaluation and threshold tuning

Evaluate on validation set using ROC AUC and PR AUC. Tune probability threshold to maximize F1 score. Calibrate probabilities using a separate calibration set or cross-validation to ensure reliable probabilities.

5. Interpretation and leakage check

Compute permutation feature importance on the validation set to understand model drivers. Identify and explain one target leakage risk, such as using future purchase history or session counts that include post-subscription behavior.

Key Points to Mention

Temporal split to respect time order and prevent lookahead bias.
ColumnTransformer with separate numeric (impute, scale) and categorical (impute, one-hot encode) pipelines.
Logistic regression with class_weight='balanced' and hyperparameter tuning over C using stratified cross-validation.
Evaluation metrics: ROC AUC, PR AUC, and threshold tuning for F1.
Probability calibration on training data only (e.g., using CalibratedClassifierCV with cv='prefit' or separate calibration set).
Permutation feature importance on validation set and explanation of a target leakage risk (e.g., using features that include future information).

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