Describe building statistical vs ML models

Q: Describe building statistical vs ML models

This question evaluates a Data Scientist's competency in building and contrasting statistical and machine learning models, encompassing feature engineering, target definition, interpretability, stakeholder communication, and operational concerns like deployment, monitoring, data leakage, and fairness within a marketing analytics context.

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Question

You’re interviewing for a Data Scientist internship on a marketing analytics team.

Tell a story about a project where you built (a) a statistical model (e.g., linear/logistic regression, GLM) and (b) a machine learning model (e.g., tree-based model, boosting, neural net).

In your answer, cover:

The business problem and decision the model supported.
The target/label definition and what “success” meant.
What features you used (behavioral, demographic/firmographic, marketing touchpoints, time-based features, text, etc.) and why.
How you handled stakeholder needs: did they only care about predictive performance, or also interpretability (which features mattered and why)?
What you would do differently next time (data issues, leakage, monitoring, deployment, fairness, etc.).

Describe building statistical vs ML models

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