About Me
I’m a data scientist working on applied ML and AI systems, currently based in Montréal. I hold a Master’s degree from Télécom SudParis (Institut Polytechnique de Paris). I enjoy building robust, practical solutions and increasingly reflecting on how data and AI are used to support meaningful, real-world decisions.
How I approach problems
I care a lot about:
- Understanding the context behind the data before modeling
- Building solutions that are robust, interpretable, and useful in practice
- Communicating uncertainty, limitations, and trade-offs clearly
- Writing clean, reproducible code and documenting decisions
- Thinking beyond model performance to consider downstream usage and impact
What I work on
- Building end-to-end data pipelines, from raw data to deployed machine learning systems
- Designing and evaluating generative AI systems, including LLM-based and retrieval-augmented approaches, for practical use cases
- Communicating results and recommendations clearly to both technical and non-technical stakeholders
- Contributing to projects where data supports decision-making at scale
Tools & technologies
- Languages: Python, SQL
- Data & Machine Learning: pandas, NumPy, scikit-learn, PyTorch
- LLMs & GenAI: RAG, LangChain, MCP, RAGAS, OpenAI SDK, HuggingFace (transformers)
- MLOps & Infrastructure: Docker, Git, GCP, MLFlow
- Visualization: Streamlit, Matplotlib, Seaborn
Resume
Want to learn more about my background and experience? You can find my resume below: