Predicting Credit Ratings with Supervised Learning
Five supervised models compared on fundamentals of US public companies. A multilayer perceptron came out on top.
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Finance × Machine Learning
William Bourgeois
Quantitative Developer and co-founder of CoPod. William builds machine learning systems for investment management, combining a background in finance with graduate training in computer science.
CFA
CFA Program: Levels I, II, III completed. Featured by the CFA Institute for a dual expertise in finance and computer science.
MSc
Master of Science in Computer Science, Machine Learning stream, at McGill University. BSc in Computer Science with distinction from UVic.
Quant
Quantitative Developer at Desjardins Securities, building Python models and multi-LLM research systems for portfolio management.
CoPod
Co-founder of CoPod, an agentic AI platform for building pods of specialized agents in any field. Proudly Canadian.
A purpose-built agentic AI layer, one that actually understands your field: its products, its rules, and its customers, changes what a single team can do.
The CoPod founding team
Discover CoPod
Selected work
Projects and research
A sample of applied machine learning and quantitative finance work.
Markowitz Mean-Variance Optimization
A Python engine that generates millions of candidate portfolios and identifies the optimal one under modern portfolio theory.
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Multi-Agent AI: Battlesnake
Competitive game AI that took 2nd place at the University of Victoria AI Club tournament.
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Featured by the CFA Institute
A non-traditional path into finance
William never saw himself as the typical finance candidate. Curiosity pushed him to explore the CFA Program, opening doors to opportunities he did not know existed and giving him a pathway into an industry he had not originally imagined working in.
After completing all three levels, he now puts his dual expertise into practice, applying both skill sets to make a meaningful impact.
Watch the feature on LinkedIn →