Data Science at Spotify with Boxun Zhang
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“I normally try to sit together or very close to a product team or engineering team. And by doing so, I get very close to the source of all kinds of challenging problems.”

Spotify is a streaming music service that uses data science and machine learning to implement product features such as recommendation systems and music categorization, but also to answer internal questions.

Boxun Zhang is a data scientist at Spotify where he focuses on understanding user behavior within the product.

Questions

• What is the overlap between distributed systems and data science?

• How has Spotify’s big data architecture evolved over time?

• As a data scientist do you need to understand this big data architecture well?

• What were the benefits for starting to use Kafka?

• What kinds of data science problems do you tackle at Spotify?

• Could you describe what a random forest is?

• Why are there so many streaming systems, and what do you use at Spotify?

• How will data science change moving towards the future?

Links

• The Evolution of Big Data at Spotify)

• Luigi)

• Project Jupyter)

• XGBoost)

• Automatic Statistician)

• Skytree)

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