Arnaud Joly
Applied Science Manger at Amazon
Greater Cambridge Area United Kingdom
Summary
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Rockstar🎓
Top SchoolArnaud Joly is an applied science manager based in the Greater Cambridge Area with 13 years of experience applying machine learning and engineering rigor to production problems. He progressed from a machine learning PhD and research fellowship at the University of Liège into impactful scientist roles at Amazon and now leads applied science efforts there. Arnaud combines deep technical chops in model development and testing with practical systems experience from early engineering and quality-management roles. An active contributor to scikit-learn, he has improved model implementations and feature-importance tooling in a widely used open-source ML library. Known for balancing code readability with robust functionality, he brings both research-grade methods and production discipline to deliver reliable ML solutions.
13 years of coding experience
15 years of employment as a software developer
MSc, Master in Electrical Engineering, MSc, Master in Electrical Engineering at University of LiègeSecondary school, Option: Mathematics, sciences, English, Secondary school, Option: Mathematics, sciences, English at Collège St Louis
English, French, Dutch
Stackoverflow
894reputation
111kreached
10answers
0questions
flask
top-5%
Github Skills (17)
python10
data-science10
scikit10
machine-learning10
scikit-learn10
feature-selection9
algorithm9
algorithms9
implement9
flask9
unit-testing8
cross-validation6
gunicorn6
svm6
numpy6
Programming languages (2)
Github contributions (5)
scikit-learn/scikit-learn
Nov 2012 - Nov 2015
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:586 commits, 41 PRs, 19 pushes in 2 years 11 months
Contributions summary:Arnaud's commits primarily involve enhancing the scikit-learn library by adding features related to random seed arguments and improving the implementation of various machine learning models. They contributed to the development and testing of the model's feature importance computation. The commits demonstrate a focus on code readability and functionality improvements within the existing framework of the library.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
arjoly/random-output-trees
Aug 2014 - Dec 2015
Contributions:101 commits, 2 PRs, 10 pushes in 1 year 3 months
regressionrandomizedmultilabel
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