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Frontline Data Science: Human + Machine

November 2018    by Charles S. Crow IV

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In this Dynamic Insight, the Chief Data Scientist at Weiss explains the unique role of the firm's Data Science team and provides forecasts on the fast-moving landscape of data analytics and machine learning on Wall Street.

Weiss is a 40-year-old firm  known for long/short discretionary investing.  Our flagship fund seeks absolute, uncorrelated returns for our investors.  The Weiss Data Science (DS) team provides data-driven analytics to the firm.  We seek to extract useful insight from raw data, influencing the Allocation Committee and individual investment strategies with their own investment processes.  Our goal is to improve risk-adjusted returns across our funds.

As the representative of the Weiss Data Science team, I am asked regularly about the team’s role and responsibilities within the firm and how we expect to evolve.  Allocators often ask these questions from the perspective of getting to understand our firm and investment process.  Their questions are often also accompanied by a hint of curiosity and self-reflection about their firm’s own data science practices (or lack thereof).  Most modern firms are groping to establish best practices and strengthen the integration between data analytics and decision makers.  The playbook is not straightforward, but there are clear practices that work and those that do not.  We hope to provide readers with actionable ideas that will help with their organization’s own data-driven journey.

This paper begins with details on how we conduct DS at Weiss, what it is (and is not), and provides rationale for our unique design choices.  I conclude with (human-generated) forecasts on the evolution of DS on Wall Street.

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