Brodie Gay is a financial data scientist who combines large computer clusters with state-of-the-art machine learning to price and forecast financial assets. He currently works at Unison Home Ownership Investors where he developed a pricing engine to continuously appraise and forecast the prices of over 100 million unique homes in America. Brodie graduated from UC Berkeley with a bachelor's degree in engineering physics and a master's of financial engineering (MFE) degree. He is a lecturer for Eric Reiner's "Quantitative Methods in Derivatives Pricing" course and teaches graduate-level workshops in machine learning and parallel computing. He worked as a quantitative strategist for Goldman Sachs' financial institutions group where he priced insurance policies and optimized bank balance sheets. Before entering the field of finance, he designed and drafted patents at a boutique IP firm in his hometown of Toronto.
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