Johann Martin
Data Scientist
Nantes, Pays de la Loire, France
Summary
🤩
Rockstar🎓
Top SchoolJohann Martin is a data scientist based in Nantes with five years of experience building production-ready ML solutions across finance, automotive and media. He has delivered fraud detection models at BNP Paribas, customer and product recommendation systems and a market-price engine for VPauto, and has industrialized ML workflows with APIs, MLflow and Databricks. Comfortable across the stack, he pairs statistical rigor from a Master in Mathematics & Statistics with hands-on engineering—data migrations, model deployment, monitoring and daily re-training pipelines. Johann also contributed to the open-source explainability library Shapash, reflecting an interest in transparent ML and practitioner-focused tooling. Curious by nature, he often bridges analytical research and pragmatic product needs, mentoring juniors and translating business problems into automated, measurable solutions.
5 years of coding experience
7 years of employment as a software developer
Baccalauréat, Economique et Social - Option Mathématiques - Option Arts Plastiques, Baccalauréat, Economique et Social - Option Mathématiques - Option Arts Plastiques at Lycée Saint JosephMaster, MATHEMATICS AND STATISTICS, Master, MATHEMATICS AND STATISTICS at Université Rennes 2
Stackoverflow
1reputation
0reached
0answers
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Github Skills (16)
lime10
transparency10
xai10
interpretable-machine-learning10
python9
shap9
machine-learning-models9
machine-learning9
iml9
deep-learning7
recommender6
data-science6
matrix-factorization4
recommender-system4
hybrid2
Programming languages (2)
Github contributions (3)
johannmartin/shapash
Jan 2021 - Mar 2021
Shapash makes Machine Learning models transparent and understandable by everyone
Contributions:8 pushes, 5 branches in 1 month
fairness-mlpythontransparentdata-sciencedeep-learning
MAIF/shapash
Nov 2020 - Feb 2021
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Contributions:2 reviews, 3 commits, 9 PRs in 3 months
explainable-mlmachine-learning-modelsimlpythontransparent
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