Alexandre Batisse
Software Engineer at Canonical
Villeurbanne, Auvergne-Rhône-Alpes, France
Summary
👤
Senior🎓
Top SchoolAlexandre Batisse is a software engineer with 11 years of experience, currently at Canonical, who blends backend engineering with applied data science and a clear ambition to transition into data science roles. He has led technical teams and client studies in the pharmaceutical sector as a Data Scientist & Tech Lead, building reproducible PyData-based workflows, Dockerized services and FastAPI endpoints. At Worldline he helped design a production-grade ML platform for training, versioning and deploying models, and contributed to real-world fraud and recovery detection projects. An active open-source contributor, he improved testing and robustness in prominent projects like Plotly Dash and scikit-learn, migrating tests to pytest and hardening ML utilities. Based in Villeurbanne, France, he pairs curiosity-driven exploration with pragmatic engineering, often focusing on maintainability and reproducible pipelines behind the scenes.
11 years of coding experience
6 years of employment as a software developer
Ingénierie des télécommunications, Ingénierie des télécommunications at INSA Lyon - Institut National des Sciences Appliquées de Lyon
French, English, German
Github Skills (20)
pytest10
python10
scikit10
testing10
machine-learning10
flask-ask10
dashjs10
scikit-learn10
flask10
data-analysis10
data-science9
web-application9
statistics9
react9
webapp9
Programming languages (9)
Github contributions (5)
plotly/dash
Jun 2019 - Aug 2019
Data Apps & Dashboards for Python. No JavaScript Required.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:20 commits, 3 PRs, 19 comments in 2 months
Contributions summary:Alexandre primarily focused on improving the application's configuration and testing infrastructure. Their contributions include fixing the server name setting, adding missing tests for application configurations, and converting existing tests to pytest. These changes suggest a focus on improving the robustness and maintainability of the application code base. Furthermore, the user migrated base component tests to pytest, indicating a transition toward a more modern testing framework.
pythonweb-appchartingflaskbioinformatics
scikit-learn/scikit-learn
Jan 2020 - Feb 2020
scikit-learn: machine learning in Python
Role in this project:ML Engineer
ML EngineerContributions:5 commits, 7 PRs, 20 comments in 4 days
Contributions summary:Alexandre primarily contributed to the codebase by enhancing and maintaining machine learning related utilities and tests within the scikit-learn repository. Their work involved refactoring code to use internal utilities, modifying tests to improve stability, and ensuring the correct handling of data types for machine learning algorithms. The contributions focused on improvements to the functionality and robustness of various machine learning models.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
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