Created and instructed “FoundationZ of Data Science,” a comprehensive online course, hosted on the AthlyticZ learning platform, which introduces the key principles of data science in Python through the lens of sports-related problem solving. Also developed supplementary materials and led tutorials for “BreeZing Through the Tidyverse,” a comprehensive educational program taught in the context of sports examples which focuses on learning the foundations of R and the Tidyverse toolkit.

Responsible for facilitating tutorial sessions and grading homework assignments and midterm/final exams for various calculus and statistics courses at Queen's University. The aim of all tutorial sessions is to further explain and apply the core concepts of the course through example problems. Other duties include exam proctoring and attending tutorial preparation meetings.

Developed and implemented Python and R code to wrangle, clean, and analyze both NFL and NHL play-by-play data, focusing on player and team performance tracking over the course of the season. Applied machine learning models to predict week-to-week game outcomes for team and player prop betting applications.

Completed data analysis and insight reports for multiple facets of the construction management software business. Worked closely with the payment processing team for reporting on the outbound customer payments and builder risk insurance initiatives. Communicated results directly to business leaders through meetings and presentations.

Tutored undergraduate students in mathematics, computer science, and data analytics courses through Grand View University's ALT Center. Specialized in calculus, linear algebra, mathematical reasoning, and introductory data analytics.