Brodie GayVP, Research at Unison
Brodie applies machine learning, statistics and quantitative methods in financial derivatives pricing to forecast and price residential real estate derivatives. In addition, he manages the quantitative research and portfolio optimization strategy, leveraging mass property level transaction databases and large-scale parallel processing clusters. Prior to Unison, Brodie worked as a Quantitative Strategist for the Financial Institutions Group at Goldman Sachs in New York City.
Brodie received his Master of Financial Engineering (MFE) and Bachelor of Science in Engineering Physics from UC Berkeley. He lectures a summer course on Quantitative Methods in Derivatives Pricing and a Machine Learning workshop at UC Berkeley for students in the MFE program.