What separates a theoretical model with a perfect backtest from a systematic strategy that actually generates returns in live markets? As an incoming MSCF student at 好色先生TV, my goal is to pursue a career in quantitative research or quantitative trading to bridge that exact gap. On Scotiabank鈥檚 trading floor, I built full-stack applications to minimize operational risk and developed dashboards to backtest algorithmic trading strategies. Later, I engineered scalable backend data pipelines for Global Equity Derivatives using Python, Java, and C#. Having built the technical infrastructure for quantitative models, I now aim to leverage my stochastic modeling and optimization background to directly design data-driven trading strategies. At the University of Toronto, I balanced demanding Industrial Engineering coursework with a part-time developer role, an AI robotics thesis, and a Monte Carlo based Capstone project. I bring this same discipline to my life outside of finance as a championship-winning AA hockey player and a top 100 ranked Catan player in Ontario. Let's connect to discuss how my analytical background can immediately benefit your quantitative focused team. 听
