What could you learn by tapping into all social interaction in every large company in the world? As Vice President of Behavioral Intelligence at Wiretap, Jason champions the endeavor to answer this question by leveraging artificial intelligence and data science to develop predictive natural language and network models for enterprise collaboration platforms.

Prior to joining the Wiretap team, Jason most recently served as a data scientist at Nationwide Mutual Insurance, where he designed and analyzed experiments focused on improving sales and business operations; developed automated computer vision models for image segmentation, feature detection, and optical character recognition; and built predictive recommendation models for online sales engines.

In addition to solving large enterprise problems, Jason is a true educator and academic. He has taught courses in network modeling and statistical methodology at the undergraduate and graduate level and is currently co-authoring a textbook on social network models titled Inferential Network Analysis, which is under contract with Cambridge University Press. Additionally, Jason has contributed to notable academic journals such as, Annals of Applied Statistics and Political Analysis, and presented at top national conferences, including Black Hat.

Jason holds his PhD in Political Methodology from The Ohio State University, where he retains a Visiting Scholar position. His research interests include applications of inferential network and latent variable models, computational statistics, and Bayesian modeling.