Check out Data Platform Explained Part I, where we started sharing the journey of building a data platform, its building [...]
Published by Anastasia Khlebnikova (Senior Engineer) and Carol Cunha (Product Manager)
TL;DR Sometimes we cannot estimate the required sample size needed to power an experiment before starting it. To alleviate this [...]
Published by Mårten Schultzberg, Sr. Manager, Staff Data Scientist
As engineers working at Spotify, we frequently find ourselves explaining our robust data platform to fellow professionals who are contemplating [...]
Published by Anastasia Khlebnikova (Senior Engineer) and Carol Cunha (Product Manager)
TL;DR We summarize the findings in our recent paper, Schultzberg, Ankargren, and Frånberg (2024), where we explain how Spotify’s decision-making [...]
Published by Mårten Schultzberg (Sr. Manager, Staff Data Scientist), Sebastian Ankargren (Sr. Data Scientist), and Mattias Frånberg (Sr. Data Scientist)
Large sets of diverse data present several challenges for clustering, but through a novel approach that combines dimensionality reduction, recursion, and supervised machine learning, we’ve been able to obtain strong results.
Published by Gustavo Pereira, Sr. Data Scientist
For the past decade, Spotify has used approximate nearest-neighbor search technology to power our personalization, recommendation, and search systems. These [...]
Published by Peter Sobot, Staff ML Engineer
When we want to determine the causal effect of a product or business change at Spotify, A/B testing is the [...]
Published by Bradley Doll (Insights Manager), Jeffrey Li (Insights Manager), and Tolga Oztan (Sr. Data Scientist)
At Spotify, we run a lot of A/B tests. Most of these tests follow a standard design, where we assign [...]
Published by Benjamin Elbers, Data Scientist
As companies mature, it’s easy to believe that the core experience and most user needs have been resolved, and all [...]
Published by Gabriella Ljunggren, Data Scientist
TL;DR: Spotify is releasing a new commercial product for software development teams: a version of our homegrown experimentation platform that [...]
Published by Tyson Singer, Head of Technology and Platforms
In Part 1 of this series, we introduced the within-unit peeking problem that we call the “peeking problem 2.0”. We [...]
Published by Sebastian Ankargren (Sr. Data Scientist), Mattias Frånberg (Sr. Data Scientist), and Mårten Schultzberg (Sr. Manager/Staff Data Scientist)
Spotify’s approach to challenges in sequential testing with longitudinal data At Spotify, we’re constantly improving our data infrastructure, which means [...]
Published by Sebastian Ankargren (Sr. Data Scientist), Mattias Frånberg (Sr. Data Scientist), and Mårten Schultzberg (Sr. Manager/Staff Data Scientist)
Messaging at Spotify At Spotify, we use messaging to communicate with our listeners all over the world. Our Messaging team [...]
Published by Euan Dowers, Senior ML Engineer
In the fast-paced world of streaming, personalization plays a vital role in enhancing user experiences. At Spotify, our Home serves [...]
Published by Nik Goyle, Product Manager
TL;DR Sequential tests are the bread and butter for any company conducting online experiments. The literature on sequential testing has [...]
Published by Mårten Schultzberg (Staff Data Scientist) and Sebastian Ankargren (Sr. Data Scientist)
We did a couple of new things in Wrapped this year, and one of these is a thing called Your [...]
Published by glenn mcdonald, Data Alchemy
TL;DR Understanding algorithmic impact is critical to building a platform that serves hundreds of millions of listeners and creators every [...]
Published by Henriette Cramer (Head of Algorithmic Impact, Director of Trust & Safety Research) and Amar Ashar (Senior Researcher, Lead Algorithmic Impact & Equity)
Some of the most common questions asked when it comes to work with performance are, How do you convince stakeholders [...]
Published by Vinicius Teixeira Dallacqua, Engineer
At Spotify, we’ve tried to uphold the tradition of running one major holiday marketing campaign at the end of each [...]
Published by James La Broy, Senior Engineer
TL;DR: Using the properties of the Poisson bootstrap algorithm and quantile estimators, we have been able to reduce the computational [...]
Published by Mårten Schultzberg (Senior Data Scientist) and Sebastian Ankargren (Data Scientist)
TL;DR Within Spotify, we run 20,000 batch data pipelines defined in 1,000+ repositories, owned by 300+ teams — daily. The [...]
Published by Guillaume Perchais, Sr. Product Manager
TL;DR For years, Spotify’s official engineering blog has been giving you a peek behind the curtain at Spotify R&D. Today, [...]
Published by Dave Zolotusky, Principal Engineer
At Spotify, we aim to build and improve our product in a data-informed way. To do that, teams are encouraged [...]
Published by Claire Detilleux, Sr. Data Scientist
Tell us more about working on Spotify Wrapped… I’m part of the Mambas team at Spotify, which is responsible for [...]
Published by Spotify Engineering