Expertise and career highlights

Thomas has deep expertise in machine learning, optimisation and statistical modelling. Since joining SFA (Oxford), he has focused on designing and deploying artificial intelligence and data-driven systems that enhance the firm’s analytical capabilities across metals markets.

As Senior Machine Learning Engineer, Thomas leads the development of SFA’s agent-based learning frameworks for critical minerals, significantly strengthening the organisation’s ability to model market behaviour and evaluate strategic outcomes. He is driving the expansion of SFA’s technology infrastructure, including secure bespoke LLM-based systems, robust database environments and wider machine learning applications used across the business.

Alongside his role at SFA, Thomas is a Machine Learning Researcher at UCL, where his work centres on optimisation techniques for global supply chains. His research spans geo-mapping optimisation, the identification of trading bottlenecks using Particle Swarm Optimisation, the application of Large Neighbourhood Search and Convex Optimisation for routing and cost modelling, and the use of Mixed Integer Programming for infrastructure design. He collaborates with international teams and contributes to research aimed at strengthening the supply chains of fast-moving consumer goods across India, Africa, and South America.

Before joining SFA, Thomas worked at VSB Group as a Data Scientist specialising in machine learning, deep learning and forecasting. His work included time series forecasting for wind and solar, optimisation of large-scale battery storage systems, the development of agentic Genetic Algorithm bidding logic, intraday trading pipelines combining machine learning and neural networks, and hybrid SARIMAX ML approaches for predicting grid frequency imbalances.

Thomas holds an MSc in Business Analytics from University College London, where he specialised in Machine Learning, Applied AI Engineering, Predictive Analytics and Optimisation. Thomas has a BA in Philosophy, Politics and Economics from the University of York.