Graham is a senior data scientist and financial researcher, with over 25 years’ experience as one of Wall Street’s original Data Scientists. He has a doctorate from Oxford University in Experimental Elementary Particle Physics, where his field of research was statistical cosmic ray astronomy which featured large scale computer-based data analytics in an era where that was uncommon.
He joined Morgan Stanley in London in 1994, where he worked on derivatives pricing and then was an early member of the now famous Process Driven Trading unit (PDT) run by Peter Muller in New York. Within PDT he managed the Futures Group, developing and managing systematic trading systems for financial futures and futures-options. He also worked on theoretical models of optimal trading strategy.
In 2000, he founded his own investment fund, Giller Investments (New York), LLC, as a Commodity Pool Operator/Commodity Trading Advisor and was involved in various quantitative proprietary trading ventures over the next 12 years.
In 2012—2013 he built an innovative platform for geospatially aware bidding in Google ad auctions for an internet marketing agency and in 2013 was recruited to Bloomberg LP to lead the data science effort within the Global Data division. This role included many board level meetings (including meetings with Mike Bloomberg) to define a vision for how Bloomberg LP could create value added premium content using predictive analytics as well as the delivery of systems to enhance operational efficiency through machine learning.
Graham joined J.P. Morgan in 2015 as Chief Data Scientist, New Product Development, and he was appointed Head of Data Science Research in 2016. At JP Morgan he co-authored the U.S. patent System and Method for Prediction Preserving Data Obfuscation.
He was recruited to Deutsche Bank as Head of Primary Research to create the unit that incorporated alternative data into analyst research content. While there he built systems for “nowcasting” important US macroeconomic statistics, including Non-Farm Payrolls, Average Hourly Earnings, Consumer Sentiment and Inflation Expectations, all based on private survey research and with results available up to three weeks before the public releases. He led the team that delivered: Market and Opinion Research, Public Data Capture, Social Media Analytics and ad hoc projects including a system to robotically interrogate Amazon’s Alexa devices for brand positioning intelligence.
Graham is a published author and acclaimed technical speaker, having written Adventures in Financial Data Science and Essays on Trading Strategy. His first book was Electronics: principles and applications, published while a graduate student in the U.K. For the past two years he has written a regular column in WILMOTT Magazine, and has recently started work on a new book provisionally titled Financial Data Science in Python. Unlike the prior finance books, this will feature extensive "walkthroughs" of the analytics performed, with helpful hints and finesses, while retaining the scientifically rigorous nature of the analysis presented in the prior works.
