Abstract: 

This talk discusses some of the principles of creating robust data science capabilities in an asset management firm based on my personal experience as well as those of my contacts in other firms. I will outline the key ingredients of a data science stack, which will also include a discussion on (1) the modern data science technology and how they are compared to traditional tools used by investment professionals, (2) machine learning techniques and statistical methods, and (3) how to attract and maintain talents. The talk will be concluded with my personal perspective on potentials of using data science in investment management and the potential challenges ahead.

Bio: 

Jeffrey is the Chief Data Scientist at AllianceBernstein, a global investment firm managing over $500 billions. He is responsible for building and leading the data science group, partnering with investment professionals to create investment signals using data science, and collaborating with sales and marketing teams to analyze clients. Graduated with a Ph.D. in economics from the University of Pennsylvania, he has also taught statistics, econometrics, and machine learning courses at UC Berkeley, Cornell, NYU, the University of Pennsylvania, and Virginia Tech. Previously, Jeffrey held advanced analytic positions at Silicon Valley Data Science, Charles Schwab Corporation, KPMG, and Moody’s Analytics.