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Determine Value of Prioritizing Accounts by Unread Notifications

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in product analytics and experimentation, covering exploratory data analysis, causal inference, metric selection, segmentation, and A/B test design.

  • medium
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Determine Value of Prioritizing Accounts by Unread Notifications

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Technical Screen

##### Scenario A product team wants to introduce a feature that, for users with multiple accounts, orders those accounts by descending count of unread notifications. ##### Question How would you determine, using data, whether prioritizing accounts by unread-notification count is a worthwhile idea? Design an A/B experiment to validate this feature. Specify population, treatment, control, success metrics, and test duration. Imagine the test shows that the frequency of account switching drops while the total number of active accounts rises. How would you interpret this result and decide next steps? ##### Hints Think about exploratory historical analysis, customer behavior segmentation, primary vs. guard-rail metrics, experiment power, and reconciling conflicting short-term and long-term signals.

Quick Answer: This question evaluates a data scientist's competency in product analytics and experimentation, covering exploratory data analysis, causal inference, metric selection, segmentation, and A/B test design.

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Meta
Aug 4, 2025, 10:55 AM
Data Scientist
Technical Screen
Analytics & Experimentation
3
0

Feature Validation: Ordering Multiple Accounts by Unread Notifications

Context

Users can hold multiple accounts and switch between them. The product team proposes to order a user's accounts by descending count of unread notifications to help them find the next most relevant account quickly.

Definitions (for clarity):

  • Account switching: The user changes from one account to another within a session.
  • Active account (per period): An account with at least one meaningful action (e.g., session, post, message, read/clear notification) in the period.

Tasks

  1. Using data, outline how you would assess whether prioritizing accounts by unread-notification count is a worthwhile idea before shipping (e.g., exploratory/historical analyses, segmentation).
  2. Design an A/B test to validate the feature. Specify:
    • Population and unit of randomization
    • Treatment and control
    • Primary and guardrail metrics (with rationale)
    • Test duration and power considerations
  3. The hypothetical result shows: frequency of account switching drops, while total number of active accounts rises. Interpret this outcome and propose next steps.

Hints

Consider exploratory historical analysis, customer behavior segmentation, primary vs. guard-rail metrics, experiment power, and how to reconcile conflicting short-term and long-term signals.

Solution

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