Chloé Pariente

I come from Centrale Paris where I have always oriented my courses and projects towards Data Science. I carried out a 6-month project in the Data Science department on wine recommendation using Infrared spectroscopy data. I then took courses in Machine Learning and Natural Language Processing, which allowed me to develop my skills and knowledge of libraries such as Scikit Learn and NLTK, as well as the SQL language. I then dedicated my gap year to coding and Data Science, where I completed various internships, notably at BNP and in a startup named Squadded, where I deepened my knowledge in NLP, Computer Vision, and recommendation algorithms. To complete my studies, I went to the United States to pursue a Master's degree in Data Science at MIT (Massachusetts Institute of Technology), where I learned the theory and application of various Machine Learning algorithms (Tree Ensemble, Regressions, etc.) as well as optimization methods. I also learned new languages such as R and Julia, as well as project management and management methods, always in the field of data analysis. During this program in Boston, I had the opportunity through courses and meetings to work on several Data Science research projects within companies such as Oracle, Zoetis, and Brigham Women's Hospital, where I further honed my skills on Machine Learning algorithms through various applications ranging from marketing and revenue optimization to disease prediction.