One of the most comprehensive LinkedIn DS Product Cases!
Overview
This question evaluates product analytics, metric definition and instrumentation, diagnostic analysis, experimentation design, and predictive modeling skills within the Analytics & Experimentation domain, requiring both conceptual understanding and practical application.
LinkedIn
Apr 30, 2025, 3:13 AM
Data Scientist
Onsite
Analytics & Experimentation
7
0
LinkedIn Profile Completion: Measurement, Diagnosis, and Improvement
Context: You are a Data Scientist working on LinkedIn's Profile experience. "Profile completion rate" should be defined precisely, instrumented, and improved through analysis and experimentation. Assume you can track field-level events (e.g., photo, headline, experience, education, skills), sessions, device, and notifications.
Answer the following:
- How would you define and measure "profile completion rate" on LinkedIn?
- If you notice a drop in profile completion rate over the last quarter, how would you diagnose the root cause?
- Propose two different product solutions to improve profile completion and describe how you would test their effectiveness.
- What kinds of data points or user behaviors would you analyze to understand why some users don’t complete their profiles?
- Which statistical or machine learning methods could help predict users at risk of not completing their profiles, and how would you use these predictions?
- Once you’ve implemented changes to improve profile completion, how do you measure success and ensure improvements are sustained over time?
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