2 of 2 Fraud Detection Jobs in the North of England

Job summary 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 … 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 … 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. About us We have offices based in Coventry, Newcastle and London and offer flexible, hybrid, office and home-based More ❯

your own projects, drive modelling initiatives, and take ideas from concept to production You'll be encouraged to propose new approaches and explore creative ways to detect and prevent fraud We debate and critique our ideas in a healthy, supportive team You'll have the chance to shape both models and how we think about fraud detection … that builds, evaluates and deploys machine learning models to improve and automate decision making Collaborate with technical and non-technical teams to understand problems, explore data, and develop effective fraud prevention tools and solutions Design and maintain robust feature engineering pipelines for modelling, working closely with analytics engineering teams Contribute to the development of end-to-end machine learning … warehouses Desire to build interpretable and explainable ML models (using techniques such as SHAP) Desire to quantify the level of fairness and bias machine learning models Enthusiasm for improving fraud detection systems and a proactive, problem-solving mindset Interview process Interviewing is a two way process and we want you to have the time and opportunity to get More ❯