Liberty. Equality. Data. Podcast Episode #5

Prifina is thrilled to welcome Dr. Peter Cotton as our special guest in the fifth episode of the "Liberty. Peter currently serves as the Senior Vice President and Chief Data Scientist at Intech Investment Management LLC. D. degree in Mathematics from Stanford, he held leadership roles at major U.S. financial institutions. Peter has led data science projects at Morgan Stanley, J.P. Morgan Chase, and several major hedge funds, where he built solutions solving complex data problems. He has extensive experience with crowdsourcing models and helped build one of the first in the world privacy-preserving computation mechanisms at J.P. Morgan Chase. In this podcast we talk about algorithms and innovation, with a focus on financial data. How does a hedge fund normalize messy financial data to build bespoke predictive models? What are the current trends and challenges related to machine learning in the financial services industry? From the innovation point of view, what happens when we cut down the cost of building algorithms to minimum functionality? Is it possible to build personal AI systems for small and mid-cap companies as well as individuals? We also delve into geeky topics, such as how to build a financial probability model for the pricing of vanilla bonds. You can find this podcast on Spotify, Apple Podcasts, Google Podcasts, and SoundCloud. He noted that one of the main areas of focus in his career was to level the playing field in the machine learning (ML) space. He notes that one of the first things to do before building financial prediction, ML, and AI models is create a system that helps clean and normalize data. "It is the most interesting mathematical problem of all because the cleaning of data implies that you understand the market itself.