About Me

I'm a data scientist working at the intersection of physics and machine learning, with a focus on energy and industrial applications.

My background combines an M.S. in Data Analytics Engineering (Northeastern University), a B.S. in Physics (UMass Lowell), and an M1 in Nuclear Engineering at Institut Polytechnique de Paris – ENSTA Paris, where I hold the EDF–Institut de France Scholarship for Nuclear Energy Studies.

Most recently, I've been a research intern at the Arts et Métiers I2M Laboratory (CNRS UMR 5295) in Bordeaux, developing physics-informed neural network (PINN) models for fatigue-life prediction in additively manufactured metallic components, applying ML in settings where data is scarce, noisy, and high-stakes.

I'm particularly interested in problems where the physics matters: energy forecasting, grid optimization, predictive maintenance, and hybrid simulation or ML approaches.

I'm currently based in Île-de-France and looking for data scientist or ML engineer roles in the Paris energy and industrial tech space.

Native English speaker with strong written French (B2) and working spoken proficiency.

Technical Skills:

  • ML & Scientific Computing: Physics-informed neural networks (PINNs), regression, classification, NLP/LSTM, scikit-learn, PyTorch, TensorFlow/Keras, SciPy

  • Programming: Python (pandas, NumPy, matplotlib, Plotly, seaborn, NLTK), SQL (MySQL), C++

  • Data & Visualization: ETL, data cleaning, Tableau, Jupyter, Google Colab

  • Tools: Git/GitHub, VS Code, MongoDB, HubSpot, Salesforce

  • Languages: English (native), French (B2 written, working spoken)