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The Interview Process
SQL/Python Take-Home
Analyzing a simulated dataset of user listening habits to answer open-ended questions like, 'Is our new podcast discovery feature actually cannibalizing music listening?'
Analytical Screen
Live SQL coding and deep discussion of your statistical methodologies, specifically focusing on causal inference and A/B testing design.
Presentation & Behavioral
You must present your take-home findings to a panel. Spotify heavily weights 'Data Storytelling'—your ability to make data visually appealing and easily understandable for leadership.
Real Spotify Interview Questions
Practice these exact questions faced by previous Data Scientist / Product Insights candidates.
1We noticed a 10% drop in overall listening hours on iOS devices in Germany over the weekend. You have 30 minutes to investigate before updating the VP of Product. Walk me through your exact analytical steps. (Problem Solving / Troubleshooting)
2Explain the concept of 'Simpson's Paradox' to me and give a hypothetical example of how it could cause us to misinterpret the results of an A/B test on a new playlist UI. (Statistics / Communication)
3Write a SQL query to categorize users into 'Casual', 'Regular', and 'Super' listeners based on their rolling 28-day listening volume, and find the churn rate for each cohort. (SQL / Data Manipulation)
4Tell me about a time your data findings were completely ignored by a decision-maker. Why did it happen, and what did you learn about 'Data Storytelling' from that experience? (Sincerity / Impact)
5How would you quantitatively determine the 'value' of a podcast listener versus a music-only listener to Spotify's overall business model? What metrics would you build? (Business Acumen)
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