At Learnd Labs, we set out to connect the entire company estate to our cloud platform, enabling IoT telemetry collection across 1 in 15 large, non-residential buildings in the UK.

Build and maintain a platform capable of handling high-ingress, high-volume data from connected devices.

Deliver a cross-platform mobile application, tailored for field use by engineers and estate managers alike.

Optimise and maintain a supervisor application, deployed on-site to enable real-time data collection and transmission from Building Management Systems (BMS).

Led the creation of a protective layer for a high-traffic e-commerce platform, designed to identify and block botnet activity and scalper behavior. Reduced peak load on core services by ~30% through a performant, serverless Go implementation.

This was an interesting one because I was able to leverage Go generics to entirely decouple authentication from the protected service—without splitting it into a separate microservice. This allowed the company to reuse this code in every project going forward.

With this type of work it really is an arms race. Some of the most engaging moments came from poring over request logs with the team and seeing what novel methods the bad actors had come up with next!

I developed and maintained a high-traffic, serverless RESTful API serving over 100,000 monthly active users. Built with a decoupled, service-oriented architecture, it was horizontally scalable and reliably absorbed large traffic spikes—especially during peak events like Black Friday.

This was my first time using Go in a professional setting. Initially, I was unsure about the lack of traditional inheritance, which I had grown to love (and probably overuse). But over time, I came to appreciate the flexibility of its compositional approach to object oriented programming.

The API was fully versioned and well-documented, enabling long-term support for native mobile clients and smooth collaboration with frontend teams. Infrastructure was provisioned as code to ensure reproducibility and alignment across environments.

I started on this project as an API developer but was quickly moved to the data team to apply my physics background to more analytical challenges. This was my first experience with data science, and it turned out to be some of the most exciting work I've done.

We started by scraping the Companies House API to build a graph database containing every officer and Person of Significant Control (PSC) associated with 4.3 million UK businesses. I then helped design and implement algorithms within our ETL pipeline to link these individuals and entities, generating a connected graph of UK corporate structures.

This graph became the foundation for detecting ownership chains and identifying patterns indicative of financial crime—including money laundering and tax evasion.

Completed a Master of Astrophysics with First-Class Honours at the University of Liverpool. My thesis, titled “Photometry and Spectroscopy of the Type II Supernova ASASSN-16a,” combined observational and computational techniques to analyse how supernovae enrich their host galaxies.

I specialised in Cosmology and Galaxy Morphology, diving deep into the large-scale structure of the universe and the classification of features of the universe at large-scale. Python was my go-to tool throughout, particularly for data analysis and visualisation.

One highlight was a module in Computational Astrophysics, where we simulated black hole binary mergers—an using a blend of numerical methods and physics.