Applied Mathematics student at York University, Toronto. I build the whole path from raw data to decision: ingestion, a tested warehouse, a model, and a dashboard someone actually uses.
Currently: Sports Analyst Intern, York University Athletics. Varsity football game databases in Python and SQL, player performance dashboards in Power BI for coaching staff.
Open to: Winter 2027 (Jan to Apr) or 8 month (Jan to Aug 2027) co-op in data, analytics, or actuarial roles.
Question: Do Bank of Canada rate changes show up in Toronto shelter demand, and can it be forecast?
Built: ELT over five public sources into DuckDB, dbt star schema on a 433,362 row fact table, 26 automated tests.
Found: Occupancy rose about 0.64pp per 1% rate increase at a 6 month lag. SARIMA and Prophet both lost to a naive baseline over 37 fold rolling origin CV, so the forecast layer was cut rather than shipped.
Python SQL dbt DuckDB statsmodels
Question: What actually predicts overdose incidence across Toronto's 158 neighbourhoods?
Built: Geocoded 2018 to 2025 incidents, spatial diagnostics, spatial lag and error models, then Poisson, Random Forest, and Gradient Boosting under repeated k fold CV.
Found: Low income prevalence drives most of the clustering. Model error fell from 52 to 33 incidents, and the remainder traces to individual addresses below neighbourhood scale.
Python geopandas PySAL scikit-learn
Question: Can a transparent model produce daily predictions at zero cost?
Built: Scheduled GitHub Actions pipeline (daily, hourly on match weekends, 15 minute checks near kickoff) feeding a weighted factor model and a live dashboard on GitHub Pages. Ingestion fails safe by keeping prior data.
Python GitHub Actions REST APIs
Analysis: Python (pandas, NumPy, scikit-learn, statsmodels, geopandas), SQL, R Modelling: OLS and Poisson regression, Random Forest, Gradient Boosting, SARIMA, Prophet, spatial regression, cross validation Data: dbt Core, DuckDB, PostgreSQL, ETL and ELT design, automated data quality tests Reporting: Power BI (DAX, Power Query), Excel, Tableau, Streamlit Tooling: Git, GitHub Actions, Docker, pytest