Arnaud Miribel
Senior Data Scientist
Grenoble, Auvergne-Rhône-Alpes, France
Summary
🤩
Rockstar🎓
Top SchoolArnaud Miribel is a Senior Data Scientist at Snowflake with a decade of experience building interactive data apps and production ML workflows. Trained in rigorous math and engineering at CPGE and EPFL, he combines strong theoretical grounding with hands-on development in Python, Streamlit, and visualization libraries like Altair and Plotly. He is an active open-source contributor—maintaining streamlit-extras and improving Streamlit demos and a face-GAN demo—where his work focuses on UI/UX, caching optimizations, and making complex models more approachable. Based in Grenoble, he bridges data science and front-end prototyping to turn experimental models into polished, user-friendly tools. A less obvious strength is his full-stack fluency: he moves seamlessly from model performance tweaks to front-end interaction details that improve adoption and developer experience.
9 years of coding experience
Bachelor of Science (BSc), Communication Systems, Bachelor of Science (BSc), Communication Systems at Ecole polytechnique fédérale de LausanneClasse Préparatoire aux Grandes Ecoles (CPGE), Mathematics, Physics, Chemistry, Classe Préparatoire aux Grandes Ecoles (CPGE), Mathematics, Physics, Chemistry at Lycée Champollion
French, English, Italian, Chinese
Github Skills (20)
python10
ui-design10
uid10
tensorflow10
front-end-development10
streamlit10
altair10
plotly10
javascript9
markdown9
web-content9
webpages9
machine-learning9
markdown-it9
computer-vision8
Programming languages (10)
Github contributions (5)
arnaudmiribel/streamlit-extras
Aug 2022 - Jan 2023
Discover, try, install and share Streamlit re-usable bits we call "extras"!
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:31 releases, 140 reviews, 121 commits in 4 months
Contributions summary:Arnaud primarily contributed to the Streamlit-extras repository by modifying the application's user interface and implementing new features. Their work included updating the Streamlit app to add new pages, edit existing content and integrate new functionalities such as chart annotations and the "running man" toggle. Moreover, they incorporated code examples and addressed styling and configuration issues within the project's components. These modifications highlight a focus on UI/UX and Streamlit component development.
discoverpythondatapipstreamlit
streamlit/demo-face-gan
Dec 2021 - Nov 2022
A demonstration of using a live Tensorflow session to create an interactive face-GAN explorer.
Role in this project:ML Engineer
ML EngineerContributions:3 reviews, 15 commits, 4 PRs in 10 months
Contributions summary:Arnaud primarily focused on optimizing the `streamlit_app.py` file within the context of a face-GAN demo. Their contributions involved refactoring caching mechanisms using `@st.experimental_singleton` and `@st.experimental_memo`, updating model-related parameters, and removing a spinner for better user experience. The commits reflect adjustments to the image generation process and model loading, indicating an emphasis on improving application performance and user interaction with the GAN model. Additionally, the commits included co-authorship which suggests collaboration on the same tasks.
pytorchdeep-learninggenerative-adversarial-networkgantensorflow
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