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This is a medium difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at LinkedIn.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at LinkedIn during technical interviews.

Predict Impact of 'Online Indicator' Feature | LinkedIn Interview Question

Predict Impact of 'Online Indicator' Feature

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competence in product analytics, experimentation design, uplift estimation, cohort segmentation, and monitoring of engagement and privacy-sensitive metrics.

Jul 12, 2025, 6:59 PM

Data Scientist

Onsite

Analytics & Experimentation

LinkedIn Messaging: Online Presence Indicator — Analytics Design

Context

LinkedIn plans to display an online presence indicator (for example, a green dot) next to 1st-degree connections across key messaging surfaces. The goal is to increase messaging engagement and responsiveness without harming user trust or privacy. Assume we can toggle the feature at the user level and we log session activity, presence (online/offline) state, and messaging events across web and mobile.

Questions

Which user cohorts and behaviors are likely to be affected by this feature?
Propose a method to estimate the number of reachable users and the expected lift prior to launch.
How would you leverage analogous historical features to calibrate lift assumptions?
What key metrics and guardrails would you monitor during rollout?

Solution

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