Data scientist and statistician. I find out what actually drives results, not just what moves with them.
Four years turning messy, real-world data into decisions for Microsoft, Unilever, P&G and BIC, from pricing and market analysis to audience and media strategy. Now a PhD researcher in Statistics at the University of Edinburgh, specialising in causal inference, and bringing that rigour to customers, campaigns and clinical data alike.
My career, plotted the way a statistician would: each bar is a role or degree, from start to finish.
University of Edinburgh
Tutorials and academic support for undergraduate and postgraduate students. Also a Postgraduate Research Ambassador since late 2025.
Polymorph Labs
Built analytics systems to identify learning gaps and automate performance reporting, improving student engagement and comprehension by about 30%.
Maverick Research
Market research for consumer goods brands including BIC, Unilever and Fanmilk across 15+ cities.
Carat (Dentsu)
Media and audience analytics for Microsoft, P&G, Beiersdorf and Betway.
Applied work in Python and R, each built on real data and written up so you can check every number.
When a campaign targets its best customers, a naive comparison overstated its impact by about 60%. Checked against a randomised experiment on 64,000 customers, AIPW and TMLE, implemented from scratch, recover the true effect with honest confidence intervals.
Python, scikit-learn, causal inference, simulation
On 41,188 real telemarketing calls, targeting the model's top 30% of clients wins 2.5 times as many term-deposit subscriptions as random calling on the same budget, and keeps 93% of the profit with 70% fewer calls.
Python, XGBoost, random forest, SHAP, business case modelling
An analysis of 15,249 BoardGameGeek games, with an app that predicts how a new game would be rated. Release year is the strongest predictor, and many popular mechanics lose their advantage once year and length are held constant.
R, tidyverse, glmnet, ranger, Shiny running in the browser
Methods for separating cause from correlation when you can't run an experiment.
Estimating the causal effects of genetic variants on disease from observational UK Biobank data, including settings where data are missing in non-random ways. The focus is on doubly robust, targeted estimators that stay reliable when models are misspecified.
Contributed the C-TMLE implementation, integrating it into the open-source Julia package TMLE.jl.
A large-scale comparison of TMLE, C-TMLE variants and scalable C-TMLE against GLM and GLMnet baselines under correctly specified and misspecified models, with cross-fitted nuisance models and jackknife inference chosen for its coverage.
A related public study: TMLE and C-TMLE in high-dimensional data (R)
I'm usually at a table somewhere: playing tabletop RPGs like the Cosmere RPG and City of Mist, or working through a stack of board games. The rest of the time I'm reading, comic books very much included, and I review most of what I read on Goodreads.