Data scientist interview: walk me through how you would build a churn prediction model.

Model answer

Walk through the lifecycle end to end, showing you think beyond the algorithm. Start with the problem definition — what counts as churn and over what window — because a vague target makes the whole model meaningless; agree it with the business. Next, data: gather usage, transactions, support contacts, and tenure, and be careful to use only information available before the prediction point to avoid leakage. Then feature engineering — trends in activity, recency, frequency, and changes in behaviour often matter more than raw values. Choose a model appropriate to the need, starting simple with logistic regression or a tree-based model for interpretability, and split the data properly, ideally by time, to reflect reality. Evaluate with metrics that fit an imbalanced problem — precision, recall, and the precision-recall curve rather than plain accuracy — and tie the threshold to what the retention team can act on. Crucially, close the loop: a model's value is the action it drives, so explain how scores feed targeted retention offers, and plan to monitor and retrain as behaviour drifts. This shows you see a model as a business tool, not a leaderboard score.

This is a guide to structure and approach, not a script to memorise — say it in your own words.

Practice this yourself

Type your own answer below and get it scored instantly by the same AI that powers Vachix mock interviews.