Victor S.Y. Lo

Victor S.Y. Lo
NISS Position: 
Elect Board Member
NISS Term Expiration: 
2022 Jun 30
Organization: 
Fidelity Investments
Professional Title: 
Head of Data Science & Artificial Intelligence, Workplace Investing, Fidelity Investments

Victor S.Y. Lo is a seasoned Big Data, Marketing, Risk, and Finance leader & innovator with over two decades of extensive consulting and corporate experience employing data-driven solutions in a wide variety of business areas, including Customer Relationship Management, Market Research, Advertising Strategy, Risk Management, Financial Econometrics, Insurance, Product Development, Transportation, Healthcare, and Human Resources. He is actively engaged with Big Data Analytics, causal inference, and is a pioneer of Uplift/True-lift modeling, a key subfield of data science that has been applied to areas as marketing, political election, and medicine.

Victor has served as the manager of quantitative teams in multiple organizations. He is currently Head of Data Science and Artificial Intelligence in Workplace Solutions at Fidelity Investments. Previously he managed advanced analytics teams in Personal Investing, Corporate Treasury, Managerial Finance, and Healthcare and Total Well-being at Fidelity Investments. Prior to Fidelity, he was VP and Manager of Modeling and Analysis at FleetBoston Financial (now part of Bank of America), and Senior Associate at Mercer Management Consulting (now Oliver Wyman).

For academic & industry services, Victor has been a visiting research fellow and corporate executive-in-residence at Bentley University. He has also been serving on the steering committee of the Boston Chapter of the Institute for Operations Research and the Management Sciences (INFORMS) and on the editorial board for two academic journals. He is a frequently invited speaker at several conferences, bridging the gap between industry and academia. Victor has a master’s degree in Operational Research from Lancaster University and a PhD in Statistics from the University of Hong Kong, and was a Postdoctoral Fellow in Management Science at the University of British Columbia. He is a co-editor of a graduate level econometrics book, published numerous articles in Data Mining, Marketing, Statistics, and Management Science literature, and is finishing a graduate level text book on causal inference for business.

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