Microsoft·Data Scientist·Technical Phone Screen·Senior

Senior

May 2026

Summary

Microsoft data scientist interview with a meaty end-to-end ML case study on a customer purchase prediction problem. One question, but it had about five sub-questions buried inside it, so budget your time accordingly.

Questions Asked (1)

You ran a marketing campaign to 10,000 customers and 500 purchased (5% conversion rate). Walk through your full approach to building a model that identifies which customers are most likely to buy, covering feature engineering, handling class imbalance, threshold selection, validation strategy, and feature selection.

Product Analytics & MetricsTechnical Trade-offsSystem Design

Author's notes

This is a monster of a question and I kind of underestimated how many pieces they actually wanted covered.

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AI HintsAI Generated

Suggested Approach

Start by framing the problem as a binary classification task with a highly imbalanced dataset, then walk through each component systematically: feature engineering, class imbalance handling, threshold selection, validation strategy, and feature selection. Emphasize the business context—maximizing ROI by targeting likely buyers—and justify your choices with trade-offs.

Pro tip: Don't just list techniques; explain how you'd align the model with business goals by choosing an evaluation metric like lift or profit curve, and discuss how threshold selection depends on campaign costs and customer lifetime value.

1. Define the problem and evaluation metric

Clarify the business objective (e.g., maximize conversions or profit) and select an appropriate evaluation metric such as AUC-ROC, PR-AUC, or lift, considering the class imbalance.

2. Feature engineering

Create features from customer demographics, past purchase behavior, engagement metrics, and campaign interactions; consider aggregations, transformations, and domain-specific features.

3. Handle class imbalance

Address the 5% conversion rate using techniques like resampling (SMOTE, undersampling), class weights, or anomaly detection methods, and evaluate their impact on model performance.

4. Model selection and validation

Choose models robust to imbalance (e.g., tree-based ensembles) and use stratified k-fold cross-validation with proper stratification to ensure representative splits.

5. Threshold selection and feature selection

Optimize the decision threshold based on business costs/benefits, and perform feature selection using methods like permutation importance or recursive feature elimination to reduce overfitting and improve interpretability.

Key Points to Mention

Class imbalance techniques: SMOTE, class weights, and their trade-offs
Evaluation metrics: PR-AUC, lift, and profit curves over accuracy
Stratified k-fold cross-validation to maintain class distribution
Threshold optimization using cost-benefit analysis or F1 score
Feature selection methods: filter, wrapper, embedded, and importance scores
Business impact: aligning model outputs with marketing ROI and targeting strategy

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