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 the United States. 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 the financial institutions group at Goldman Sachs 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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