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

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

Adjust YouTube Ad Scores Using Mixed-Effects Linear Regression

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of mixed-effects (hierarchical) linear regression and the ability to adjust for rater bias, testing competencies in statistical modeling, fixed-versus-random effects distinctions, and handling hierarchical rating data.

Google

Jul 12, 2025, 6:59 PM

Data Scientist

Technical Screen

Machine Learning

Scenario

100 reviewers each rate the same 100 YouTube ads on a 1–10 scale.
Ratings may be systematically higher or lower for some reviewers (leniency/severity bias).

Goal

Produce unbiased ad scores (comparable on a common scale) by removing reviewer bias.

Task

Propose a modeling framework to adjust for reviewer bias and justify why a mixed-effects linear regression is appropriate.

Assumptions

Each rating is on an interval-like scale (1–10). Treating it as approximately continuous is acceptable; see alternatives for ordinal modeling.
We want ad-specific scores (so ads are fixed effects) and to treat reviewers as a sample from a larger reviewer population (random effects).

Solution

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