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Assess Need for Group Calls

Last updated: Apr 2, 2026

Quick Overview

This question evaluates product analytics, experiment design, metric engineering, and causal inference skills, testing a candidate's ability to define and interpret engagement, adoption, retention, and quality metrics from call and DAU data.

  • hard
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Assess Need for Group Calls

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Technical Screen

A messaging platform currently supports only one-to-one voice calls and is considering whether to launch **group calling**. You have access to the same data sources used in the SQL exercise: **Table: `call_events`** - `call_id` BIGINT - `event_time` TIMESTAMP - `caller_user_id` BIGINT - `recipient_user_id` BIGINT - `country_code` STRING - Assume one row per completed call attempt **Table: `daily_active_users`** - `activity_date` DATE - `user_id` BIGINT - `country_code` STRING The interviewer asks two product analytics questions: 1. **How would you use these tables to determine whether users need a group calling feature?** 2. **If the company launches group calling, how would you determine whether the launch was successful?** Provide a structured answer that covers: - What user behaviors in the existing one-to-one call data could signal latent demand for group calls - How you would define candidate metrics, including tradeoffs between adoption, engagement, retention, and call quality - What success metrics and guardrail metrics you would use after launch - Likely confounders, selection bias, and interpretation risks - How you would design an experiment or quasi-experiment if a randomized launch is possible or not possible

Quick Answer: This question evaluates product analytics, experiment design, metric engineering, and causal inference skills, testing a candidate's ability to define and interpret engagement, adoption, retention, and quality metrics from call and DAU data.

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Meta
Jan 2, 2026, 12:00 AM
Data Scientist
Technical Screen
Analytics & Experimentation
2
0

A messaging platform currently supports only one-to-one voice calls and is considering whether to launch group calling.

You have access to the same data sources used in the SQL exercise:

Table: call_events

  • call_id BIGINT
  • event_time TIMESTAMP
  • caller_user_id BIGINT
  • recipient_user_id BIGINT
  • country_code STRING
  • Assume one row per completed call attempt

Table: daily_active_users

  • activity_date DATE
  • user_id BIGINT
  • country_code STRING

The interviewer asks two product analytics questions:

  1. How would you use these tables to determine whether users need a group calling feature?
  2. If the company launches group calling, how would you determine whether the launch was successful?

Provide a structured answer that covers:

  • What user behaviors in the existing one-to-one call data could signal latent demand for group calls
  • How you would define candidate metrics, including tradeoffs between adoption, engagement, retention, and call quality
  • What success metrics and guardrail metrics you would use after launch
  • Likely confounders, selection bias, and interpretation risks
  • How you would design an experiment or quasi-experiment if a randomized launch is possible or not possible

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