What’s your main area of work now for Project Athena?
As a data scientist, I work on a variety of tasks. Lately my focus has been on data quality and clustering techniques to help uncover potential anomalies or fraud patterns. My role involves cleansing data and ensuring reliability, so our models work with the most accurate information possible.
Why do you value working on Project Athena?
In my previous roles before working at the NHSCFA, I used data science to improve profit margins for companies. Now, I am using those same skills to protect NHS funds and ensure resources go back to patient care. That’s incredibly rewarding.
Project Athena is also unique because we are laying the foundations for fraud detection in the NHS. It’s exciting to be at the start of something that will have a long-term impact.
How did you first get into data science?
I started my career in materials science and engineering, focusing on manufacturing optimisation. As a senior engineer, I analysed processes and identified inefficiencies using advanced statistical methods. Over time, I became more interested in how data could drive better decision-making, which led me to specialise in machine learning and data science.
One of my biggest breakthroughs was developing a machine learning model that could predict the strength of materials by proxy without the use of traditional mechanical destructive methods, cutting inspection times by 95%. That experience showed me how data can transform decision-making and I knew I wanted to apply these skills on a larger scale.
Tell us about your career pathway and highlights so far.
I began in manufacturing engineering, optimising production lines and workforce movement — where I first saw the value of data-driven decisions. I then moved into aerospace, presenting material selection work at Cambridge University, before transitioning to a start-up environment where I led projects in automation, materials science, and data-led process development.
In recent years, I’ve focused on digital transformation — building systems to replace manual data collection and securing funding from sources like the Midlands Aerospace Alliance. A standout achievement was helping shift a sceptical operations team to one that fully embraced data to solve everyday manufacturing challenges.