Name
Shannon Terry
Organization/Company Affiliation (full name)
Nationwide
Position Title
VP And Chief Advanced Analytics Officer
Speaker Bio
Shannon is an applied statistician as well as an artificial intelligence (AI), machine learning (ML), and data management expert. Educated as a statistician with a focus on social research, he went on to pursue graduate studies in information systems and statistics. His career began outside of Nationwide in retail analytics where he learned to work with large databases and build complex analytic software systems.
In the nineties, he worked as a lead designer and programmer of multimedia technologies for a software start-up. When he joined Nationwide at the end of the nineties, he spent the first few years in financial services learning the products and operations. He then moved into information technology and data warehousing for several years before joining Nationwide’s Customer Insights & Analytics team in 2008. As part of this team that would eventually become the Enterprise Analytics Office, Shannon helped to introduce the R statistical programming language, scientific Python programming language, and early machine learning concepts to Nationwide starting in 2009. He helped design and build Nationwide’s proprietary and patented Model Factory in 2010 and was one of the authors of the Nationwide Analytic Science job family. In 2013, he built Nationwide’s first production artificial neural network.
Over the years, Shannon has worked on a variety of statistical and AI/ML models including both patented and patent-pending solutions. He is a member of the American Statistical Association and has authored several software packages for advanced modeling and statistical analysis including bodies of novel, independent research presented at the annual Joint Statistical Meetings, the largest gathering of statisticians in North America.
Shannon currently leads the Enterprise Analytics Office, a team founded in early 2018 with Analytic Scientists and Analytic Engineers possessing doctoral and master’s degrees in mathematics, statistics, machine learning, engineering, physics, economics, operations research, and other quantitative disciplines. His team focuses on building high-performance, end-to-end, and at-scale statistical and AI/ML solutions – including both traditional and generative – for our business leaders across the full enterprise in areas such as underwriting, pricing, reserving, claims, servicing, investments, telematics, innovation, and fraud detection.
In the nineties, he worked as a lead designer and programmer of multimedia technologies for a software start-up. When he joined Nationwide at the end of the nineties, he spent the first few years in financial services learning the products and operations. He then moved into information technology and data warehousing for several years before joining Nationwide’s Customer Insights & Analytics team in 2008. As part of this team that would eventually become the Enterprise Analytics Office, Shannon helped to introduce the R statistical programming language, scientific Python programming language, and early machine learning concepts to Nationwide starting in 2009. He helped design and build Nationwide’s proprietary and patented Model Factory in 2010 and was one of the authors of the Nationwide Analytic Science job family. In 2013, he built Nationwide’s first production artificial neural network.
Over the years, Shannon has worked on a variety of statistical and AI/ML models including both patented and patent-pending solutions. He is a member of the American Statistical Association and has authored several software packages for advanced modeling and statistical analysis including bodies of novel, independent research presented at the annual Joint Statistical Meetings, the largest gathering of statisticians in North America.
Shannon currently leads the Enterprise Analytics Office, a team founded in early 2018 with Analytic Scientists and Analytic Engineers possessing doctoral and master’s degrees in mathematics, statistics, machine learning, engineering, physics, economics, operations research, and other quantitative disciplines. His team focuses on building high-performance, end-to-end, and at-scale statistical and AI/ML solutions – including both traditional and generative – for our business leaders across the full enterprise in areas such as underwriting, pricing, reserving, claims, servicing, investments, telematics, innovation, and fraud detection.