Joel Persson — Research Scientist, AI & Economics, Spotify

I am a Research Scientist in the AI & Economics lab at Spotify. I work on statistical and machine learning methods for causal inference and data-driven decision-making. I am particularly interested in practical problems in experimentation, personalization, and policy evaluation, as well as the use of algorithms and digital technologies in business and public organizations more broadly.

At Spotify, I lead research and development in how to improve the efficiency and effectiveness of experimentation and evaluation in recommender systems, including how AI can augment traditional approaches. The methods and tools I have developed are used internally for product decision-making 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 was also the first technical advisor to Algorithm Audit, a Dutch NGO that audits high-impact algorithms and AI systems for bias, discrimination, and ethical risks.

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.