Optimize Churn Prediction: Feature Engineering and Model Selection

Q: Optimize Churn Prediction: Feature Engineering and Model Selection

This question evaluates a data scientist's competency in large-scale feature engineering, model selection and tuning, model explainability, and production debugging for a weekly churn prediction pipeline operating on millions of users.

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Question

Weekly Churn Prediction (10M users): Feature Engineering, Model Choice, Explainability, and Debugging

Scenario

You own a weekly churn-prediction pipeline that trains on 10 million users. The goal is to predict who will churn so the business can target retention interventions.

Tasks

Feature Engineering
- Define the label, observation/prediction windows, and leakage controls.
- Propose key feature families and how to handle imbalance.
Model Selection and Hyper-parameter Tuning
- Describe the model development process, evaluation, and tuning strategy at this scale.
Model Choice Rationale
- Why might you favor Gradient Boosted Trees (GBTs) over Logistic Regression (LR) here?
Explainability
- Describe two techniques for explaining model outputs to non-technical stakeholders.
Production Debugging
- If recall drops by 15% week-over-week, provide a step-by-step debugging checklist.

Hints: Discuss imbalance handling, SHAP, feature drift, and offline/online parity checks.

Optimize Churn Prediction: Feature Engineering and Model Selection

Weekly Churn Prediction (10M users): Feature Engineering, Model Choice, Explainability, and Debugging

Scenario

Tasks

Solution

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