Summary
Elijah Cavan is an NFL data scientist with a decade of experience translating advanced mathematics and statistical theory into sports analytics and product-ready insights. He holds a MSc in Mathematics from Wilfrid Laurier and a Mathematical Physics degree with an astrophysics specialization from the University of Waterloo, and has additional graduate training in statistics—bringing deep theoretical rigor to practical modeling. His career spans analytics roles across professional sports (NFL, MLB, NHL), startups, and retail, where he built models and R&D pipelines that informed scouting, in-game strategy, and business decisions. Comfortable bridging research and production, he has moved from teaching and academic research into fast-paced applied analytics roles, demonstrating an ability to explain complex methods to nontechnical stakeholders. Based in Toronto, he combines interests in quantum field theory, general relativity, and cosmology with hands-on data science, a mix that fuels creative approaches to probabilistic modeling and feature engineering. Colleagues describe him as a mathematically curious practitioner who thrives at the intersection of theory, code, and competitive sports.
10 years of coding experience
2 years of employment as a software developer
Master's degree (Co-Op), Statistics, Master's degree (Co-Op), Statistics at Simon Fraser University
Masters of Science, Mathematics, Masters of Science, Mathematics at Wilfrid Laurier University
Mathematical Physics w/ Astrophysics specialization, Physics, Mathematical Physics w/ Astrophysics specialization, Physics at University of Waterloo
French, English