How do you turn mathematical training into financial impact while balancing engineering school and a demanding co-op? Over the past two years, I have explored this question by studying AI at T茅l茅com Paris while working as a Data Scientist at BNP Paribas. I am pursuing quantitative research roles where I can use mathematical modeling and machine learning to support investment decisions under uncertainty. At BNP Paribas, I applied Python, machine learning, and NLP to large-scale financial problems, including AML alert reduction, model robustness analysis, and NLP-based query routing. My academic work in deep learning, graph-based forecasting, and probabilistic modeling has strengthened my ability to move from rigorous mathematics to practical models. My perspective is shaped by resilience and curiosity. Three years in the French Classes Pr茅paratoires system (MPSI/MP*), one of France鈥檚 most intensive STEM preparatory tracks, trained me to learn fast under pressure. At T茅l茅com Paris, organizing student and cross-school events taught me to connect people across backgrounds. I am eager to connect with employers looking for a fast-learning candidate ready to contribute to quantitative research and investment teams. 听
