Javier Zaurin
Staff ML AI Engineer at Nansen
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolJavier Zaurin is a Staff Machine Learning Engineer based in London with over 20 years of research and industry experience and 11 years focused in commercial ML roles, specializing in recommendation systems and NLP across fashion, retail, insurance, automotive, gaming and web3. He transitioned from a decade in academia (PhD in Astronomy & Astrophysics) to lead ML teams and build production-grade models, recently tackling RecSys and moderation for crypto applications. Javier is the creator and main maintainer of the pytorch-widedeep open-source library, with a JOSS publication highlighting its practical impact on wide-and-deep modelling. Known for bridging deep technical expertise with product thinking, he combines hands-on model engineering, open-source stewardship and cross-domain forecasting and synthetic-data experience to move prototypes into scalable services.
11 years of coding experience
17 years of employment as a software developer
Degree & Master's Theoretical Physics, Degree & Master's Theoretical Physics at Universidad de ZaragozaDoctor of Philosophy (Ph.D.) Astronomy and Astrophysics, Doctor of Philosophy (Ph.D.) Astronomy and Astrophysics at The University of Sheffield
Spanish, English
Github Skills (131)
python10
deep-learning10
text-processing10
blending10
r10
sublime-text10
gradient-boosting10
lightgbm10
multiclass-classification10
kaggle10
classification10
multimodal-deep-learning10
text-classification10
kafka-python10
text-mining10
Programming languages (10)
Github contributions (5)
jrzaurin/neural_cf
Oct 2018 - Jun 2019
Comparing keras, pytorch and gluon using neural collaborative filtering
Contributions:34 commits, 29 pushes, 1 branch in 8 months
collaborative-filteringkeraspytorchdeep-learninggluon
jrzaurin/tabulardl-benchmark
Apr 2021 - Jun 2021
Benchmark tabular Deep Learning models against each other and other non-DL techniques
Contributions:19 commits, 4 PRs, 15 pushes in 2 months
deep-learning
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