Last updated July 2026 — currently finishing my MSc dissertation at LSE, open to data science and analytics opportunities from September 2026.
I build data-driven tools and models that turn complex information into clearer insights. I’m completing an MSc in Data Science at the London School of Economics (graduating September 2026), where my dissertation develops an augmented Elo rating system for football match outcome prediction — combining a four-stage optimisation pipeline (backpropagation through time, ISTA, SAGA, cross-validation) to beat canonical Elo on out-of-sample log-loss. I hold a BSc in Computer Science and Statistics from St. Lawrence University.
My broader project work spans Bayesian inference on football league data, interactive Shiny applications for F1 telemetry, graph neural networks for urban mobility forecasting, and distributed ML pipelines on GCP — hands-on experience across the full data science stack, from messy real-world data to deployed models.
I’m drawn to data science and analytics roles where I can work as part of a team to uncover patterns in real-world data and translate them into decisions that matter, with a particular interest in sports, betting, and motorsport analytics. Though, I am open to strong opportunities across industries. Outside of technical work, I’ve sharpened my ability to communicate ideas to different audiences through competitive swim coaching and academic mentoring, skills I can bring directly into a new team.
If anything here interests you, feel free to reach out!
Education
The London School of Economics and Political Science | London, UK MSc in Data Science | Sept 2025 – Sept 2026
St. Lawrence University | Canton, NY, USA BSc in Computer Science and Statistics | Aug 2021 – May 2025
Skills
Languages: Python, Java, SQL, R
ML & Analytics: PyTorch, Apache Spark MLlib, Scikit-learn, PySpark, BigQuery, Bayesian Inference, Machine Learning, Deep Learning
Visualisation: Shiny, Plotly, Seaborn, ggplot2, Dash, Tableau
Tools & Infrastructure: Docker, PostgreSQL, GCP, Flutter, Git