Steven De Gryze
Machine Learning Engineering Manager at Meta
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolSteven De Gryze is a machine learning engineering manager with 13 years of multidisciplinary experience leading teams that deliver production-grade recommender and ranking systems. He has led high-impact teams at Stitch Fix driving deep learning and generative-model-powered apparel recommendations that generated tens of millions in revenue and ran 50+ A/B tests to optimize user experience. At The Climate Corporation he combined scientific modeling and engineering leadership to deliver fertilizer recommendations and satellite-based yield prediction at scale, and he has hands-on experience across Python, PyTorch, TensorFlow, Clojure and Scala. Now leading ranking engineers in Monetization at Meta, he balances roadmap strategy, uptime and rigorous experimentation with an empathetic, people-first management style. An early open-source contributor to scikit-learn compatibility work, he brings both academic rigor (PhD-level environmental science and statistics training) and practical systems-building chops to bridge research and production. Outside work he’s as likely to be found pitching a tent as debugging a model, a hint at his appetite for long-term thinking and field-tested problem solving.
13 years of coding experience
5 years of employment as a software developer
PhD Environmental Sciences, PhD Environmental Sciences at KU Leuven
French, Dutch, English, German
Github Skills (8)
scikit-learn10
data-analysis10
machine-learning10
python10
data-science10
scikit10
testing8
numpy8
Programming languages (2)
Github contributions (5)
scikit-learn/scikit-learn
Mar 2013 - Mar 2013
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:6 commits, 2 PRs, 5 comments in 1 day
Contributions summary:Steven's commits primarily focus on adapting existing code for Python 3 compatibility within the scikit-learn library. This involves converting string literals to bytes using the `six` library, addressing type errors, and ensuring compatibility across Python versions, specifically targeting the dataset loading and processing functionalities. The user also made changes within the test suites, updating string comparisons and fixing UTF-8 encoding issues. Their contributions ensured the library's functionality and maintainability within a changing Python ecosystem.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
sdegryze/cloudpickle
Apr 2015 - Apr 2015
Contributions:2 PRs, 13 pushes, 13 branches in 5 days
pythonextended
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