Building a New Financial Data Set from Scratch with Active Learning and NLP
Building a New Financial Data Set from Scratch with Active Learning and NLP


S&P Global is on a mission to power the markets of the future, and we're using state-of-the-art machine learning technologies to do it. In this talk, Lead Data Scientist Zach Anglin will address how the AI Engineering team builds human-in-the-loop machine learning workflows to create a new financial data set focused on environmental and sustainability factors from scratch, in an environment with a dollar-figure guarantee on data accuracy. Technologies highlighted include spaCy, BERT, and active learning.


Zach Anglin is a lead data scientist in the AI Engineering department at S&P Global, where he focuses on problems in natural language processing and probabilistic machine learning. He's particularly passionate about numerical optimization and the Julia programming language. Zach lives in Charlottesville, Virginia with his wife, Kylie, and their dog, Boolean.

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