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

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