Stephen Haptonstahl
About Stephen Haptonstahl
Stephen Haptonstahl is the Director of Data Science at Transfix, with a diverse background in academia and industry, including roles at Capital One and NPR. He specializes in economic machine learning and advocates for autistic individuals, reflecting his commitment to social causes.
Current Role at Transfix
Stephen Haptonstahl serves as the Director of Data Science at Transfix, a position he has held since 2022. In this role, he leads data science initiatives and oversees the development of machine learning models that support the company's operations. His leadership aims to enhance the efficiency and effectiveness of data-driven decision-making processes within the organization.
Previous Experience at Capital One
Prior to his current role, Stephen worked at Capital One in various capacities. He served as Director of Data Science for Connect ML from 2020 to 2022 and for Conversational AI & Messaging from 2019 to 2020. Additionally, he held the position of Senior Manager of Data Science from 2017 to 2019. His work focused on implementing data science strategies that improved operational outcomes and customer engagement.
Academic Background and Education
Stephen has a diverse academic background. He earned a Bachelor of Science in Mathematics from Louisiana State University, followed by a Master of Science in Mathematics with a focus on measure theory from Northern Illinois University. He completed his PhD in Political Science at Washington University in St. Louis, where he studied American politics, behavioral economics, statistical methods, and game theory.
Commitment to Community Involvement
Stephen advocates for autistic individuals, demonstrating his commitment to community involvement and social causes. His advocacy reflects a dedication to supporting underrepresented groups and contributing positively to society.
Problem-Solving Expertise
Stephen is recognized for his problem-solving abilities, capable of addressing both longstanding issues and complex new challenges. He specializes in economic machine learning, focusing on predictive models related to pricing, costing, and other economic factors. His experience includes leading a lean data science team that delivered machine learning models into production at a higher rate than typical teams.