How did you get started in this field?
I completed my PhD in machine learning in New Zealand before joining NEC Corporation in Denmark as a data scientist. NEC’s focus on public sector AI solutions, including healthcare and digital government, provided me with hands-on experience in applying machine learning at scale. The role deepened my expertise in AI-driven analytics and set the foundation for my career in healthcare and fraud detection.
What’s your main area of work now for Project Athena?
I’m currently conducting data-driven investigations into coding patterns and tariff practices within secondary care, focusing on identifying potential anomalies. By using advanced analytics and data science, I compare patient characteristics and clinical coding trends across different healthcare providers to detect unusual patterns. This work helps enhance transparency, ensure compliance and support informed decision-making in healthcare operations.
Why do you value working on Project Athena?
I strongly believe in giving back to the community. The NHS operates within a finite budget and the more we can protect its resources through programmes like Project Athena then the better we can support the population. Data science plays a crucial role in achieving this goal.
Tell us about your career pathway and highlights so far
In Denmark, I built a machine learning model to predict youth unemployment, analysing factors like family employment, education and interview success. This enabled caseworker colleagues to tailor support and recommend pathways such as apprenticeships or further education. The initiative significantly reduced unemployment in the target group from 11.6% to 5%, demonstrating the power of AI in driving data-informed policy and social impact.
At the Institute of Cancer Research within the Royal Marsden Hospital I led a project developing machine learning algorithms for early breast cancer detection. Our system analysed MRI data and lab evidence and enabled radiologists to diagnose up to four times more cases per day. Knowing that my work accelerated life-saving diagnoses for patients was incredibly rewarding.
Why do you enjoy working in healthcare and data science?
I find it incredibly fulfilling to use data science to drive real-world impact, especially in healthcare. Solving complex challenges with AI and machine learning not only improves efficiency but also directly enhances patient care and public health. Knowing that my work contributes to better diagnoses, fraud prevention and fairer healthcare access makes every project meaningful and rewarding.