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.
Winfield employs advanced quantitative finance knowledge and data science skills in investment research and portfolio management. His role involves option valuation, risk modeling, and portfolio optimization activities. He provides analytical firepower, leads research projects and develops next-generation technology to support the investment decision-making process. Before joining Unison, Winfield worked on portfolio analytics and execution research at FORT L.P., a quantitative hedge fund based in Washington, D.C. Prior to that, he worked at Micron Technology where he led global process optimization projects by leveraging and analyzing mass production data using statistical and machine learning techniques.
Winfield received a Bachelor of Engineering with First Class Honors from Nanyang Technological University, Singapore and a Master of Financial Engineering from the Haas School of Business, UC Berkeley.