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Design an Experiment to Evaluate New ML Model

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competencies in online experiment design, causal inference, metric selection, assessment of statistical and practical significance, identification and handling of heterogeneous treatment effects, data-quality and marketplace interference mitigation, and ethical rollout decision-making.

  • hard
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Design an Experiment to Evaluate New ML Model

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Onsite

##### Scenario Ads platform wants to validate engineers’ claim that a new ML ranking model outperforms the existing recommender. ##### Question How would you design an experiment to evaluate the new ML recommendation system? Which primary and guard-rail metrics would you monitor and why? An A/B test shows a 5% lift in CTR—how do you judge practical significance? CTR doubles for Indian males aged 18-55—what might this indicate and what next steps would you take? If the test yields +5% CTR and +5% revenue, would you roll the model out globally? Explain your decision process. ##### Hints Discuss randomization, sample size, heterogeneous effects, business trade-offs, and ethical checks.

Quick Answer: This question evaluates a data scientist's competencies in online experiment design, causal inference, metric selection, assessment of statistical and practical significance, identification and handling of heterogeneous treatment effects, data-quality and marketplace interference mitigation, and ethical rollout decision-making.

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Meta
Aug 4, 2025, 10:55 AM
Data Scientist
Onsite
Analytics & Experimentation
1
0

Experiment Design: Validating a New Ads Ranking Model

Context

You operate an ads platform with an existing recommender/ranking model. Engineers built a new ML ranker that is hypothesized to improve outcomes. You need to run an online controlled experiment (A/B) to validate performance, choose appropriate metrics, assess significance, and make a rollout decision, including handling heterogeneous effects and ethics.

Tasks

  1. Experiment design
    • What is your randomization unit, ramp plan, duration, and sample size approach?
    • How will you guard against marketplace interference and data-quality issues?
  2. Metrics
    • Define 2–3 primary KPIs and a set of guard-rail metrics. Explain why each matters for users, advertisers, and the platform.
  3. Practical significance
    • An A/B test shows a +5% lift in CTR. How do you judge practical (business) significance vs. statistical significance?
  4. Heterogeneous effects
    • CTR doubles for Indian males aged 18–55. What might this indicate, and what next steps do you take to validate and respond?
  5. Rollout decision
    • If the test shows +5% CTR and +5% revenue, do you roll out globally? Explain your decision process, including trade-offs and ethical checks.

Solution

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