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Evaluate Carousel and Billboard Lift

Last updated: Apr 2, 2026

Quick Overview

This question evaluates experiment design, causal inference, metric definition, diagnostic analysis, and measurement validity within the Analytics & Experimentation domain for a Data Scientist role.

  • medium
  • Pinterest
  • Analytics & Experimentation
  • Data Scientist

Evaluate Carousel and Billboard Lift

Company: Pinterest

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

You are the product data scientist supporting Pinterest. Two analytics prompts were described: 1. **Carousel feature at the top of the home feed.** Pinterest wants to add a horizontally scrollable carousel at the top of the app, similar to Stories. The module could either resurface Pins the user previously saved or show new recommended Pins. How would you: - clarify the product goal, - define primary success metrics and guardrail metrics, - design an online experiment, - and decide whether the feature should launch? Suppose the carousel is launched in an experiment and the **overall home-page CTR decreases**. Explain how you would determine whether this is a true product regression versus a denominator effect, cannibalization of the existing vertical feed, ranking-quality issues, novelty effects, segmentation effects, or logging problems. 2. **Billboard experiment.** Pinterest also runs an offline billboard campaign in a subset of cities or DMAs. How would you estimate the causal lift of the campaign on app activity? Discuss the choice of experimental or quasi-experimental design, unit of randomization, key metrics, power considerations, and how you would handle spillovers, seasonality, and overlapping national marketing campaigns.

Quick Answer: This question evaluates experiment design, causal inference, metric definition, diagnostic analysis, and measurement validity within the Analytics & Experimentation domain for a Data Scientist role.

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Pinterest logo
Pinterest
Jan 11, 2026, 12:00 AM
Data Scientist
Technical Screen
Analytics & Experimentation
7
0
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You are the product data scientist supporting Pinterest. Two analytics prompts were described:

  1. Carousel feature at the top of the home feed. Pinterest wants to add a horizontally scrollable carousel at the top of the app, similar to Stories. The module could either resurface Pins the user previously saved or show new recommended Pins. How would you:
    • clarify the product goal,
    • define primary success metrics and guardrail metrics,
    • design an online experiment,
    • and decide whether the feature should launch?
    Suppose the carousel is launched in an experiment and the overall home-page CTR decreases . Explain how you would determine whether this is a true product regression versus a denominator effect, cannibalization of the existing vertical feed, ranking-quality issues, novelty effects, segmentation effects, or logging problems.
  2. Billboard experiment. Pinterest also runs an offline billboard campaign in a subset of cities or DMAs. How would you estimate the causal lift of the campaign on app activity? Discuss the choice of experimental or quasi-experimental design, unit of randomization, key metrics, power considerations, and how you would handle spillovers, seasonality, and overlapping national marketing campaigns.

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