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Evaluate an ads algorithm change

Last updated: Apr 2, 2026

Quick Overview

This question evaluates competency in experiment design, causal inference, metric selection, product analytics, and evaluation of ad-ranking systems, including handling interference, auction effects, novelty and delayed conversions, segmentation, and reconciling offline and online model signals.

  • medium
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Evaluate an ads algorithm change

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

Your ads team has developed a new ad-ranking algorithm for feed delivery. The new algorithm is expected to improve ad relevance and monetization, but it may also hurt user experience, shift delivery toward a small set of advertisers, or create short-term gains that do not hold long term. How would you decide whether to launch this algorithm? In your answer, discuss: - the primary business objective and why a single metric such as CTR is insufficient - the experiment design, including randomization unit and rollout strategy - key success metrics and guardrail metrics for users, advertisers, and platform health - how to handle interference, auction effects, novelty effects, and delayed conversions - what segment analyses you would run across geography, device, advertiser size, or user cohorts - how you would proceed if offline model metrics and online experiment results disagree

Quick Answer: This question evaluates competency in experiment design, causal inference, metric selection, product analytics, and evaluation of ad-ranking systems, including handling interference, auction effects, novelty and delayed conversions, segmentation, and reconciling offline and online model signals.

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|Home/Analytics & Experimentation/Meta

Evaluate an ads algorithm change

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Meta
Feb 22, 2026, 12:00 AM
mediumData ScientistOnsiteAnalytics & Experimentation
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Your ads team has developed a new ad-ranking algorithm for feed delivery. The new algorithm is expected to improve ad relevance and monetization, but it may also hurt user experience, shift delivery toward a small set of advertisers, or create short-term gains that do not hold long term.

How would you decide whether to launch this algorithm?

In your answer, discuss:

  • the primary business objective and why a single metric such as CTR is insufficient
  • the experiment design, including randomization unit and rollout strategy
  • key success metrics and guardrail metrics for users, advertisers, and platform health
  • how to handle interference, auction effects, novelty effects, and delayed conversions
  • what segment analyses you would run across geography, device, advertiser size, or user cohorts
  • how you would proceed if offline model metrics and online experiment results disagree
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