I approach tasks with the mindset of turning academic insights and economic intuition into scalable, production-ready tools. I graduated from Texas Tech University in 2025 with degrees in Economics, Mathematics (Data Science concentration), and General Studies focused on Actuarial Science. I earned A+ marks in Ph.D.-level courses in Stochastic Calculus & Financial Derivatives and Software Engineering for Financial Applications. In my quant finance internship, I rebuild forecasting pipelines, real estate, credit, and risk models in Python, turning econometric theory into fast, readable code. In academia, I was the only undergraduate in the Financial Mathematics Ph.D. Research Group under Dr. Svetlozar Rachev, where I developed a thesis constructing dynamic factor models for U.S. equities. My work focused on extracting key signals, limiting noise, and generating unique models for each period and across all equities. I bring extensive experience in Python and R and a deep modeling intuition. I've also led the Texas Tech Economics Association, coauthored research on college football NIL valuation, and developed course materials on using text mining the NLP in economics. My goal is to bridge economic intuition and technical execution in a role that demands precision, speed, and clarity. I am available to connect via Zoom or in person to discuss how I can contribute to your team.
