Spotify Data Scientist Interview Questions

1.Describe a time when a colleague was struggling with a machine learning concept or analysis approach. How did you help them without taking over their work?

2.Describe a time when you had to choose between multiple modeling approaches for a recommendation or personalization problem, and no clear guidelines existed. How did you make that decision?

3.Tell me about a time when your squad had conflicting data interpretations and no manager stepped in to break the tie. How did you navigate that situation?

4.Walk me through a recent analysis where the data pointed in one direction, but you had a gut feeling something else was happening. How did you handle that tension?

5.Tell me about a time when you noticed your team was becoming siloed or losing its collaborative spirit. What specific actions did you take to address this?

6.Tell me about a time when you helped someone on another squad solve a data science problem they were stuck on. What was your approach?

7.Code a function that takes a user's listening history and returns their top 5 genres for Spotify Wrapped. The input is a list of tracks with timestamps and genre tags.

8.Design a simple anomaly detection system to flag unusual artist streaming patterns that might indicate playlist manipulation or bot activity. What features would you use?

9.Walk me through how you'd design an A/B test to measure the impact of a new podcast recommendation algorithm. What metrics would you track and how would you handle the fact that podcast listening behavior is very different from music?

10.You notice that Spotify Wrapped engagement dropped 15% year-over-year in a specific country. How would you investigate this using our data infrastructure?

11.You're building a personalized playlist feature for Spotify that uses real-time listening patterns. The ML model needs to retrain every hour on 600M+ user interactions. How would you architect the data pipeline using our stack?

12.Explain how you'd use Apache Spark to process daily listening events and update user preference vectors for our recommendation system. What partitioning strategy would you use?

13.Tell me about a time when your squad needed to make a major technical decision, but you didn't have a chapter lead or manager available to weigh in. How did you help the team move forward?

14.Describe a time when you introduced a new data science technique or approach to your team. How did you get buy-in and help others adopt it?

15.Tell me about a time when you had to influence a product manager or engineer in another squad to change their approach based on data insights you discovered. What was your strategy?

16.Describe a situation where your squad was moving fast on a feature launch, but you discovered data quality issues that could affect the user experience. How did you balance speed with data integrity?

17.Tell me about a time when you had to convince your squad to experiment with an unconventional approach to a personalization or recommendation problem, even though it seemed risky or unproven.

18.Describe a time when you noticed that data science insights were consistently being ignored or deprioritized by product teams. How did you diagnose the root cause and address the broader organizational challenge?

19.Spotify Premium has about 250 million subscribers globally. Estimate how many hours of music we serve per day, and what that means for our data storage costs.

20.If Spotify wanted to enter live audio conversations like Twitter Spaces, estimate the infrastructure cost for the first year assuming 5% of our DAUs try it monthly.

21.You notice that daily active users on Spotify increased 3% last week, but total listening hours only went up 1%. How would you investigate this discrepancy?

22.Estimate how much revenue Spotify loses annually from users who share Family plan accounts beyond the intended household members. Walk me through your approach.

23.A major record label threatens to pull their entire catalog unless we reduce their royalty reporting lag from 45 days to 7 days. Estimate the engineering cost to make this change.

24.Spotify Wrapped 2024 had 30% less social sharing than 2023, but user engagement within the app stayed the same. What hypotheses would you test to understand why?