Jeffrey Dumont

Senior Data Scientist

Jeffrey Dumont is an expert modeler, researcher, and data scientist. He manages the technical and analytical aspects of complex modeling and survey projects for both public and private sector clients in the United States and abroad. Since joining RSG in 2006, he has played a significant role in survey sample design, development of efficient experimental designs, and advanced statistical analysis of survey data for hundreds of projects. Jeff’s primary interests are in bringing advanced methods to solve real-world problems. Currently, he is exploring how to fuse secondary data with survey data to improve demand forecasts.

Jeff enjoys fly-fishing in Vermont’s rivers and streams and volunteering as a judge in the Vermont State Math and Science Fair. He lives in White River Junction, Vermont with his wife and two daughters.

Graduate Studies in Choice Modeling, University of Leeds, United Kingdom
BS with Honors, Mathematics, Lafayette College

Hess, S., A. Daly, J. Dumont, and Nobihuro Sanko. When Explanatory Variables Are Unobserved: Example of Latent Income. The Journal of Choice Modelling, No. 12 (2014) 47-57.

Dumont, J., M. Giergiczny, and S. Hess. Individual-level Models vs. Sample-level Models: Contrasts and Mutual Benefits. Transportmetrica A: Transport Science, Vol. 11, Issue 6, 2015.

Dumont, J. and K. Gnojek. An Analysis of the Impact of Screen Size on Stated Choice Behavior for Credit Card Applications. Presented at the International Choice Modelling Conference, May 2015.

Dumont, J., J. Keller, and N. Whipple. Understanding How Covariates Perform Across Different HB Packages. Presented at the American Marketing Association’s Advanced Research Techniques Forum, June 2015. Won Best Poster presentation award.

Magidson, J. and J. Dumont. A New Modeling Tool for Identifying Meaningful Segments and their Willingness to Pay: Improving Validity by Reducing the Confound between Scale and Preference Heterogeneity. Presented at the American Marketing Association’s Advanced Research Techniques Forum, June 2015.

Dumont J., J. Keller, and C. Carpenter. “Functions for Hierarchical Bayesian Estimation: A Flexible Approach.” R Package on Comprehensive R Archive Network, 2013.

Carpenter, C., J. Dumont, and N. Whipple. “Developing an Advanced Preference Based Simulator – A Flexible and Scalable Approach Using R and the Amazon Elastic Compute Cloud.” Awarded “Best Poster” at the Advanced Research Techniques Forum, Seattle, WA, June 2012.