As a Data Scientist at Bull SAS, I am proficient in several data science tools and techniques, and I've had the chance to make significant contributions to our projects. I've developed a recommendation system for efficient data management, implemented performance model-driven methods, and optimized workflows. My work on multivariate time series preprocessing, clustering, and classification has allowed me to deepen my skills with tools such as scipy, sklearn, and tensorflow2/keras. I am confident in my Python programming skills and have extensive experience with Git, Docker, and MongoDB.My background in scientific signal and image processing has also been invaluable. In previous roles, I developed algorithms for image and signal processing, and machine learning - involving recognition, alpha matting, classification, and segmentation. I've worked with time series models and managed data with Pandas, Numpy, and SQL/MongoDB. This experience has greatly informed my approach to data science and computation.When it comes to High Performance Computing (HPC), I've had a rewarding learning experience. I've been exposed to various aspects of HPC, including the use of slurm, burst buffers, and workflow managers like nipype. I've also had the chance to explore data placement policies and delve into operations research. Each day, I aspire to learn more about HPC and aim to strengthen my understanding and skills in this area.