Joel Persson — Research Scientist, AI & Economics, Spotify

I am a Research Scientist in the AI & Economics lab at Spotify. My research focuses on econometric and machine learning methods and their application to different areas in the digital economy. I am particularly interested in causal inference and decision-making problems in experimentation and personalization, policy learning and evaluation, and the use of digital technologies in business and public organizations.

At Spotify, I lead research and development in how to improve the efficiency and effectiveness of experimentation, evaluation, and training of large-scale recommender systems, including how AI can augment traditional approaches. The methods and tools I have developed are used internally for product innovation and run in production on the Spotify Homepage.

I received my PhD from ETH Zurich in 2024, advised by Stefan Feuerriegel and Florian von Wangenheim. During my doctorate, I visited the Operations, Information, and Technology group at Stanford GSB, hosted by Jann Spiess, and interned as Machine Learning Research Scientist at Booking.com. I also contribute to the nonprofit Algorithm Audit.

I hold master's and bachelor's degrees in Statistics and Business & Economics from Lund University, Sweden. Before my PhD, I worked on marketing mix models at GfK (acquired by NielsenIQ) and in performance marketing at Precis Digital.