Eustache Diemert
Lead Data Scientist
Grenoble, Auvergne-Rhône-Alpes, France
Summary
👤
Senior🎓
Top SchoolEustache Diemert is a Lead Data Scientist based in Grenoble with 13 years of experience blending software engineering, AI research, and people management across startups, scale-ups and big tech. He has led teams that turned causal research into measurable ad incrementality gains and shipped production ML systems from prototyping to global scale. At Criteo he supervised PhD-level research, released open datasets, and bridged academic venues (ICML/NeurIPS/ICLR) with industrial impact; more recently he combines geospatial and AI teams at PUR. A pragmatic contributor to scikit-learn, he built an out-of-core text classification example using HashingVectorizer and partial_fit to handle large datasets memory-efficiently. Interested in Innovation x ClimateTech, he pairs deep technical rigor with a track record of mentoring talent and driving reproducible, deployable research.
13 years of coding experience
14 years of employment as a software developer
MS, Computer Science, MS, Computer Science at Université de Technologie de Compiègne (UTC)
French, German, English
Stackoverflow
1,797reputation
225kreached
25answers
2questions
loops
top-5%
Github Skills (19)
python10
scikit10
machine-learning10
text-classification10
scikit-learn10
data-analysis10
data-science9
feature-extraction9
numpy9
loops9
x509certificates6
sorting6
amazon-dynamodb6
certificate6
dictionary6
Programming languages (6)
Github contributions (5)
scikit-learn/scikit-learn
May 2013 - Dec 2013
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:95 commits, 1 comment in 6 months
Contributions summary:Eustache contributed to the development of an out-of-core classification example for text documents using the scikit-learn library. Their work involved implementing feature extraction using `HashingVectorizer` and training a classifier that supports `partial_fit`, enabling memory-efficient processing of large datasets. This involved integrating components for Reuters dataset parsing, accuracy evaluation, and plotting, demonstrating a focus on building and evaluating machine learning pipelines for text classification.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
oddskool/boncoin
May 2015 - Mar 2019
Contributions:6 pushes in 3 years 10 months
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