ssp3nc3r lectures in the applied analytics graduate program at Columbia University, his alma mater. He works with core developers of Stan — a probabilistic programming language — building Bayesian, generative models to enable decision-making in complex fields such as sports performance.

His work in modeling and communication arise from a doctorate of jurisprudence, masters of science in sports management focused on data science analytics, and bachelors of science in chemical engineering focused on numerical methods and statistical process control.

The most persuasive communications are transparent and account for uncertainty, which are two areas of interest in his research and work in quantitative communication through visualization and storytelling. Along with research he has collaborated and published variously. These include analyses, editing, and research for The Real Madrid Way (BenBella Books 2016), of which Billy Bean has said “will be one of the most influential books on sports ever written.” He has a forthcoming monograph and literature review on quantitative communication amid uncertainty.

Along with honors recognition for research and writing, he has won analytics competitions, including the Society for American Baseball Research's analytics competition, graduate division. He is fluent in R, codes in Stan for Bayesian modeling, ggplot2 and D3.js for visualization. Other tools include Git, Rmd, SQL, C++, macOS, and Adobe CC.