Bridging the gap between front-line user support, robust backend software engineering, and advanced machine learning research.
Résumé by track:
My journey & philosophy
"I am a Computer Scientist deeply focused on machine learning systems at the intersection of AI and financial markets. My work revolves around building robust forecasting models, architecting trading decision logic, and engineering the resilient data infrastructure that feeds them. I specialize in time-series forecasting, reinforcement learning for trading, and creating mechanical decision layers built on top of learned forecasts."
Two examples of how I build reliable systems: production-minded data infrastructure and rigorous machine-learning research.
A live, multi-source FX data platform that normalizes six data sources into a point-in-time-correct DuckDB feature store.
6 independent workers · 5 validation gates · audited ingestion
A Django-based internal workflow that turns business requirements into structured BRS/SRS documents.
Azure document extraction · authentication · activity logging · Word/PDF export
From highly concurrent data infrastructure to state-of-the-art model training frameworks, here is my technical toolkit.
What I build: auditable data pipelines, business-facing automation, and machine-learning systems with validation built in.
3 core technologies
3 core technologies
3 core frameworks
Infrastructure & Viz
Troubleshooting, user support, POS issues, documentation, escalation, Linux, and networking.
Explore IT SupportDjango, Python, PHP, JavaScript, APIs, SQL, authentication, validation, logging, and document workflows.
Explore Software DevTime-series ML, LightGBM, Transformers, reinforcement learning, leakage controls, and trading algorithms.
Explore ML Research
Currently based in Regina, Saskatchewan.
Interested in collaboration, research, or engineering roles.