Frequent Traveler Case
Company: LinkedIn
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Onsite
How would you define a “frequent traveler”?
Which data points or features would you use to identify frequent travelers?
Why is it important to consider both frequency and distance of location changes rather than just frequency alone?
Once you’ve identified frequent travelers, how can you use this information in a product or service?
What analytical or modeling approaches might you use to classify users as frequent travelers?
What pitfalls might arise if you only focus on the frequency of location changes without considering distance?
Quick Answer: This question evaluates a data scientist's ability to operationalize behavioral and coarse geolocation signals, perform feature engineering and geospatial-temporal modeling, and link analytical outputs to product use cases and limitations.