Summary
Samuel Berry is a recent Computer Science graduate and incoming M.Sc. candidate specializing in applied modelling and quantitative methods, with nine years of hands-on experience building full-stack web apps, system-level tools, and data-driven models. He has practical expertise across Python, C++, C#, Java, JavaScript and MIPS, and has delivered a Flask+React lake health visualization for a community partner as well as a thesis-grade ML pipeline that achieved up to 90% R² for crop yield prediction using feature engineering and SHAP interpretability. As a Graduate Teaching Assistant he leads labs, runs code walkthroughs, and provides actionable feedback, pairing strong communication skills with technical mentorship. Samuel blends production-minded development with rigorous data analysis, enjoys translating messy environmental and economic datasets into usable insights, and is eager to contribute to teams working at the intersection of software engineering and quantitative modelling.
9 years of coding experience
1 year of employment as a software developer
Master of Science (M.Sc.) Applied Modelling & Quantitative Methods (Thesis Stream), Master of Science (M.Sc.) Applied Modelling & Quantitative Methods (Thesis Stream) at Trent University