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Fighting Online Fraud and Abuse with Large-Scale Machine Learning at Sift Science
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| by Jacob Burnim Follow 0 Followers on Nov 06, 2017 |

Jacob Burnim discusses Sift’s approach to building a ML system to detect fraud and abuse, including training models, handling imbalanced classes, sharing learning, measuring performance, etc..


Jacob Burnim is a tech lead at Sift Science, where he leads the machine learning team. He previously worked at Google on a machine learning system for ranking search results. Jacob has a Ph.D. in Computer Science from UC Berkeley and a B.S. in Computer Science and Mathematics from Caltech.

Data Science is an emerging field that allows businesses to effectively mine historical data and better understand consumer behavior. This type of scientific data management approach is critical for any business to successfully launch its products and better serve its existing markets.

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