Snap·Data Scientist·Technical Phone Screen
- For a weekly churn prediction pipeline running on 10M users, walk through how you'd approach feature engineering, model selection, and hyperparameter tuning.
- Why would you choose Gradient Boosted Trees over Logistic Regression for this kind of churn problem?
- Describe two approaches for explaining model predictions to stakeholders who don't have a technical background.
- Recall drops 15% week-over-week on your churn model. What's your debugging process?
“I started with feature categories (recency, frequency, engagement decay) and mentioned handling class imbalance with SMOTE vs just reweighting the loss.”