I am a Research Scientist at Spotify. My research focuses on statistical and machine learning methods for causal inference and data-driven decision-making in digital domains. At Spotify, I currently work on bandits, experimentation, and econometric methods for improving recommender systems powered by AI.

I received my PhD from the Department of Management, Technology, and Economics at ETH Zurich in 2024, advised by Stefan Feuerriegel and Florian von Wangenheim. During my doctorate, I visited the Operations, Information & Technology area at Stanford Graduate School of Business, hosted by Jann Spiess, and interned as Machine Learning Research Scientist at Booking.com in Amsterdam. I also joined the non-profit organization Algorithm Audit, where I contribute to open-source software for bias detection in machine learning systems.

I hold double bachelors and masters degrees in Statistics and Business & Economics from Lund University, Sweden, and previously worked as Marketing Scientist at GfK (now part of Nielsen) and as analyst at an award-winning digital marketing agency.