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Developed quantitative trading solutions for crypto options by applying Black-Scholes models and advanced Python scripts to analyze options pricing and volatility trends, improving forecast accuracy by 20%. Engineered automated volatility-surface generation pipelines integrating multi-source Excel and API data with Pandas and NumPy, reducing manual data processing time 40% and enabling real-time analytics. Maintained production-grade Python codebases with pytest and Git, enhancing reliability, scalability, and collaboration across concurrent development tasks.
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B.Sc. - Computer Science and Data Science - Wilfrid Laurier University (2024-09 - 2028-05)