Machine Learning Engineer focused on Personalization, Experimentation, and Business Decision-Making.
I build machine learning systems that help organizations better understand customers, improve retention, increase customer lifetime value, and make better decisions.
Background.
I am a Machine Learning Engineer at Spotify, currently based in London.
My experience spans personalization, customer understanding, experimentation, retention, and growth. I've worked on production machine learning systems and data products that influence customer experiences and business outcomes at scale.
I studied Statistics at the University of Warwick and have a strong interest in causal machine learning, constrained optimization, and decision-making under uncertainty.
Outside of work, I enjoy exploring how experimentation, machine learning, and decision systems can drive better outcomes.
Selected roles.
Machine Learning Engineer
Spotify
Building machine learning systems to personalize subscription grace periods, improving retention and net revenue.
Machine Learning Engineer
Trainline
Worked on customer lifetime value modelling and profitable growth initiatives, including predictive modelling and contextual bandits for conversion optimization.
Machine Learning Engineer (Consultant)
Guidehouse
Digital Twins for Gas Distribution Network.
Research Assistant (Medical AI)
Dept. of Medicine, University of Hong Kong
Image Classification for Orthopaedics.
What I think about.
Personalization
Tailoring product experiences to individual customers using behavioural signals and machine learning.
Growth Systems
Models and pipelines that support acquisition, conversion, and lifetime value across the customer journey.
Experimentation
Designing and analysing A/B tests so product and business teams can make confident decisions.
Decision Intelligence
Connecting predictions to actions — turning model outputs into policies, thresholds, and business rules.
Causal Inference
Estimating real effects of interventions when randomised experiments aren't feasible or affordable.
Machine Learning Infrastructure
The pipelines, feature stores, and serving systems that let models reliably influence production decisions.
Notes and essays.
Thoughts on machine learning, experimentation, growth, and decision-making.
Coming soon