Olivier Jeunen
Senior Machine Learning Scientist at Booking.com
Antwerp, Antwerp, Belgium
Summary
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Senior🎓
Top SchoolOlivier Jeunen is a Principal Research Scientist based in Antwerp with nine years of experience applying machine learning to recommendations, experimentation, and evaluation. He blends rigorous academic research—culminating in a PhD on "Offline Approaches to Recommendation with Online Success"—with industrial impact from roles at Aampe, ShareChat, Amazon, Spotify Research, Facebook, and Criteo. Olivier is skilled at translating theory into production-ready solutions and has repeatedly collaborated with both large tech labs and university spin-offs. His background spans hands-on research positions and leadership in decision science, giving him a rare view across offline evaluation and online product success. Notably, his career path shows a steady focus on recommender systems informed by cross-organizational experiments rather than purely theoretical work.
9 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science: Data Science, Doctor of Philosophy - PhD Computer Science: Data Science at University of AntwerpErasmus exchange programme Computer Science, Erasmus exchange programme Computer Science at The University of EdinburghLatijn-Wiskunde, Latijn-Wiskunde at Moretus Ekeren
Dutch, English, French, German
Github Skills (29)
advertising10
real-time-bidding10
reinforcement-learning9
simulation9
openai-gym9
criteo9
machine-learning8
recommendation-system8
pytorch7
google-cloud-platform7
collaborative-filtering7
spotify6
data-warehouse6
recommender-system5
sql5
Programming languages (4)
Github contributions (5)
olivierjeunen/ease-side-info-recsys-2020
Aug 2020 - Jul 2021
Source code for our LBR paper "Closed-Form Models for Collaborative Filtering with Side-Information" published at RecSys 2020.
Contributions:10 commits, 8 pushes, 1 branch in 11 months
collaborative-filtering
olivierjeunen/dual-bandit-kdd-2020
Jun 2020 - Jul 2021
Source code for our paper "Joint Policy-Value Learning for Recommendation" published at KDD 2020.
Contributions:7 commits, 6 pushes, 1 branch in 1 year 1 month
kddpolicyrecommendationmachine-learningjoint
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