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

What role is this question designed for?

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

Engineer ZIP Features and Handle Missingness

Last updated: Apr 2, 2026

Quick Overview

This question evaluates feature engineering with geographic data, strategies for handling missing ZIP codes and external data joins, and awareness of fairness, leakage, privacy, and validation concerns when using demographic variables.

Intuit

Feb 10, 2026, 12:00 AM

Data Scientist

Technical Screen

Machine Learning

You are building a predictive model for users, and for some users you have address data. Many records include ZIP code, but some do not. You may also join external public data, such as census-style demographic summaries, using ZIP code.

What address-derived or ZIP-linked features would you consider using as model inputs?
How would you handle missing ZIP codes?
What risks would you watch for when using geographic and demographic variables, and how would you test whether these features actually help the model?

Assume the target is not specified; answer in a general way that would be appropriate for a product-focused data science interview.

Solution

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