Apple·Data Scientist·Take-home Assignment·Senior

Senior

Jun 2026

Summary

Apple data scientist take-home that was basically a full ML system design exercise disguised as a coding prompt. One question, but it covered everything from feature engineering to post-deployment monitoring, so budget your time accordingly.

Questions Asked (1)

Build an end-to-end scikit-learn pipeline to predict 28-day churn using only features available at decision time, handling high-cardinality categoricals, class imbalance, temporal leakage, hyperparameter tuning, probability calibration, and post-deployment drift monitoring.

System DesignTechnical Trade-offsData Modeling

Author's notes

This is a lot to fit into one question.

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AI HintsAI Generated

Suggested Approach

Structure your answer as a production ML system design, walking through data preparation, modeling, evaluation, and monitoring. Emphasize how each choice addresses the specific challenges: temporal leakage, high-cardinality categoricals, class imbalance, calibration, and drift. Balance technical depth with business impact, explaining why each decision matters for churn prediction at Apple's scale.

Pro tip: Frame the pipeline as a product, not just a model: discuss how you'd version data, automate retraining, and set up alerts for drift. Mention that calibration is critical for churn because the business needs reliable probabilities to prioritize retention offers, not just rankings.

1. Data preparation and feature engineering

Use only features available at decision time to avoid temporal leakage. Split data chronologically (e.g., train on older data, validate on newer) and handle high-cardinality categoricals with target encoding or hashing, ensuring encodings are computed only on training folds to prevent leakage.

2. Model training and class imbalance

Choose an appropriate model (e.g., gradient boosting) and address class imbalance via class weights, resampling, or focal loss. Use scikit-learn's Pipeline and ColumnTransformer to encapsulate preprocessing and model steps, ensuring reproducibility.

3. Hyperparameter tuning and evaluation

Perform hyperparameter tuning with time-series cross-validation (e.g., TimeSeriesSplit) to respect temporal order. Evaluate using metrics like PR-AUC, recall at fixed precision, and calibration curves, not just accuracy.

4. Probability calibration

Calibrate predicted probabilities using Platt scaling or isotonic regression on a held-out validation set. This ensures churn probabilities are reliable for business decisions like targeting retention campaigns.

5. Deployment and drift monitoring

Deploy the pipeline with versioning and automated retraining. Monitor for data drift (e.g., PSI, KS tests) and concept drift (e.g., performance degradation), setting up alerts and a feedback loop to update the model.

Key Points to Mention

Temporal leakage: use time-based splits and ensure all features are computed only from data available before the prediction point.
High-cardinality categoricals: use target encoding with smoothing or hashing, and apply within cross-validation folds to avoid leakage.
Class imbalance: use class weights, resampling, or appropriate evaluation metrics like PR-AUC.
Hyperparameter tuning: use TimeSeriesSplit or nested cross-validation to respect temporal order.
Probability calibration: apply Platt scaling or isotonic regression to make probabilities actionable.
Drift monitoring: track data drift (PSI, KS) and concept drift (performance metrics) with automated alerts and retraining triggers.

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