Data Scientist | NHS Counter Fraud Authority

The NHS Counter Fraud Authority (NHSCFA) is the national body responsible for all matters relating to the prevention, detection and investigation of economic crime across the NHS. Further information about our work and annual plan for delivering this is available on our website.

The Data Scientist role requires expertise in machine learning, statistical analysis, anomaly detection, and strong communication and collaboration skills. Key responsibilities include developing innovative models, designing, applying, and optimising models in a dynamic environment. The role is critical in project planning, using data to drive business objectives, and defining project directions to achieve financial targets. With a deep understanding of data science, including machine learning, predictive modelling, and deep learning, the Data Scientist will tackle complex challenges and extract actionable insights from diverse datasets. They will lead model development and ensure solutions meet business and government standards.

The post holder will be required to have a NPPV2.

Potential applicants can contact Susan Proctor on susan.proctor@nhscfa.gov.uk for an informal chat if you have any questions regarding the role.

We reserve the right to close this vacancy before the advertised closing date should we receive a significant number of applications.

Interviews will be held Online on 5th August 2025.

Collaboration across functions is essential to align data science initiatives with NHSCFA goals, ensuring accountability for innovative outcomes. Utilising the latest advanced methods, they will embed analytics into the organisation to enhance fraud detection within the NHS.

Clear communication of statistical outputs and results to non-technical stakeholders is crucial, influencing decisions like criminal intervention, policy changes, or risk metrics based on data-driven insights. The role demands adherence to government data standards, ensuring data transparency, integrity, and compliance throughout the portfolio.

Prepare data for model development and selection using techniques such as, sampling, feature engineering and normalisation etc.

Leverage advanced AI and machine learning techniques, including deep learning, to improve fraud detection and prevention models while adhering to privacy regulations.

We have offices based in Coventry, Newcastle and London and offer flexible, hybrid, office and home-based working. The NHSCFA values and respects the diversity of its employees and aims to recruit a workforce which reflects our diverse communities. We welcome applications irrespective of people\’s age, disability, gender, race or ethnicity, religion or belief, sexual orientation, or other personal circumstances. We have policies and procedures in place to ensure that all applicants are treated fairly and consistently at every stage of the recruitment process, including an invitation to the first stage of the selection process and consideration of reasonable adjustments for people who have a disability. If you are applying to undertake this role on a secondment basis you should have agreement to being released from your current role in principle, prior to submitting an application form. When you apply for this role, you will be redirected to our recruitment system TRAC.The NHSCFA does not hold a sponsor licence in respect of skilled worker visas and so is unable to employ candidates requiring sponsorship.

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Create, deploy, and refine cutting-edge AI/Machine Learning algorithms and suitable methodologies to detect and anticipate evolving fraud patterns and trends.

· Design innovative solutions to solve business problems in the identification of fraud and wider NHS to protect NHS money and resources from irregular activity.

Build highly accurate practical Machine Learning models, by developing supervised, unsupervised, and semi supervised models etc, creating end-to-end data pipelines and deploying outputs within the NHSCFA environment ready for action.

Provide a deep understanding of the theoretical foundations behind classical and recent machine learning models and algorithms, such as generalised linear models, random forests, SVM, ensemble methods, and deep neural networks etc including assessment and justification of approaches and metrics and how each is used in a practical environment to detect anomalies/fraud within the NHS including providing verbal and written explanation of the results and key metrics.

Minimise false positives while ensuring predication or classification accuracy is paramount.

Deploy the models in the operational environment and maintain/troubleshoot any production issues that arise.

Please see full job description and person specification.

This advert closes on Wednesday 23 Jul 2025 #J-18808-Ljbffr