How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

This is a medium difficulty Machine Learning question, commonly asked during Technical Screen rounds at Gusto.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Gusto during technical interviews.

Build Churn Prediction and Survival Models

Last updated: May 31, 2026

Quick Overview

This question evaluates a candidate's competency in designing end-to-end churn prediction and time-to-event (survival) models across multiple model families—linear models, logistic regression, decision trees, and survival analysis—including label definition, feature engineering, evaluation metrics, interpretability, calibration, common pitfalls, and deployment considerations. It is commonly asked in Machine Learning/Data Science interviews to assess the candidate's ability to translate business retention objectives into robust modeling solutions, reason about trade-offs and biases such as data leakage and selection bias, and demonstrates both conceptual understanding and practical application of modeling, evaluation, and productionization.

Gusto

May 27, 2026, 12:00 AM

Data Scientist

Technical Screen

Machine Learning

You are asked to describe, end to end, how you would build models for retention or churn.

Cover the following model families:

Linear regression.
Logistic regression.
Decision tree models.
Survival analysis for time-to-churn modeling.

For each approach, explain the modeling workflow from problem definition through deployment. Include label definition, feature creation, train/validation/test splitting, assumptions, evaluation metrics, interpretability, calibration, common pitfalls such as data leakage and selection bias, and how the model would be used by a product or lifecycle-marketing team.

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