Data Scientist
SII Group
Total years of experience :4 years, 7 Months
- Literature review on the application of Machine/Deep Learning in the Building Energy sector.
- Creation of a project dedicated to optimize building energy efficiency by leveraging forecasts made by ML/DL models.
- Data collection, analysis and preprocessing using Python : Pandas, Numpy, Matplotlib.
- In-depth statistical analyses on Time Series data, especially energy meter and sensor data.
- Train, validate, and optimize ML/DL models like XGBoost and LSTMs using Scikit-Learn and Tensorflow.
- Model deployment on the Cloud using Amazon Sagemaker.
- Creating clear and concise visualizations to effectively communicate results to stakeholders.
- Developing Proof of Concepts (PoC).
- Project management with GitLab.
- Code versionning with Git.
- Recruiting and managing team members to promote operational efficiency.
- Literature review on the physical properties of 2D hybrid perovskites.
- Setup of a micro-photoluminescence (micro-PL) experiment.
- Measurement of the PL and time-resolved PL signal from 2D perovskite crystals.
- Analysis of experimental data using Python, Jupyter, Matplotlib, Numpy and Pandas.
- Modeling and simulation of PL decays using SciPy and LMfit.
- Presentation of research results in French and English at seminars.
- Writing of articles and the thesis using LaTeX.
- Contribution to the publication of 3 scientific articles
- Publication of a thesis demonstrating the non-linear dynamics of recombination processes, the threshold of exciton-exciton annihilation and the role of exciton-phonon coupling in 2D hybrid perovskites.
