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NHS Counter Fraud Authority
Gwybodaeth
The NHS Counter Fraud Authority (NHSCFA) is the national body responsible all for matters relating to the prevention, detection and investigation of economic crime across the NHS. Aligned to the DH Health Group Counter Fraud strategy, the NHSCFA acts as the principal lead for the NHS and wider health group in counter fraud intelligence work.
Cysylltu
- Address
- NHS Counter Fraud Authority
- 7th Floor,
- 10 South Colonnade,
- London
- E14 4QQ
- Contact Number
- 0300 330 0739
Mae newidiadau mewngofnodi ar ddod
O fis Gorffennaf 2025, bydd proses Dilysu Aml-Ffactor (MFA) yn cael ei chyflwyno i'r wefan hon. Bydd hyn yn ychwanegu cam ychwanegol at y broses mewngofnodi: bydd gofyn i chi fewnbynnu Cod Mynediad Untro (OTP) wrth i chi fewngofnodi i leihau twyll a sicrhau diogelwch. Gweler ein tudalen crynodeb yma am ragor o fanylion.
Data Scientist
Closed for applications on: 24-Gorff-2025 00:01
Statws y swydd wag: Closed
Closed for applications on: 24-Gorff-2025 00:01
Manylion allweddol
Lleoliad
- Gwefan
- 1st Floor, Citygate
- Cyfeiriad
- Gallowgate
- Tref
- Newcastle upon Tyne
- Cod post
- NE1 4WH
- Major / Minor Region
- Tyne a Wear
Math o gontract a phatrwm gwaith
- Contract
- Cyfnod Penodol: 8 mis (Fixed Term until 31st March 2025)
- Oriau
- Gweithio hyblyg
- Gweithio gartref neu o bell
Cyflog
- Cyflog
- £53,755 - £60,504 per annum
- Cyfnod cyflog
- Yn flynyddol
- Gradd
- (NHS AfC: Band 8a)
Arbenigedd
- Prif leoliad
- Data Science
The NHS Counter Fraud Authority (NHSCFA) is the national body responsible all for matters relating to the prevention, detection and investigation of economic crime across the NHS. Aligned to the DH Health Group Counter Fraud strategy, the NHSCFA acts as the principal lead for the NHS and wider health group in counter fraud intelligence work.
The NHSBSA is responsible for the processing of your application; a privacy notice is attached to advise you on how we will process your personal data.
Trosolwg o'r swydd
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 [email protected] 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.
Advert
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.
Gweithio i'n sefydliad
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.
Swydd-ddisgrifiad a phrif gyfrifoldebau manwl
·
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.
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.
All new entrants to the NHS will be appointed on the minimum of the pay scale in line with Agenda for Change Terms and Conditions.
If you are applying to undertake this role on a secondment basis you should discuss this opportunity with your manager and have agreement to being released from your current role in principle, prior to submitting an application form.
We reserve the right to close any vacancies from further submissions when we have received sufficient applications from which to make a shortlist. Please ensure you apply without delay if you wish to be considered for this role. For help with completing your application form, please read the guidance notes attached to this advert.
Please note all contact is made via our TRAC recruitment system. Please check your account regularly. If you are shortlisted for interview you will be required to provide proof of ID and the right to work in the UK.(Please refer to attached guidance documents for further details). Failure to bring the required proof will mean that we may be unable to proceed with your interview.
Please ensure you provide full contact details, including email address or fax number for each referee.
NHS Counter Fraud Authority Website: www.cfa.nhs.uk
Gofynion yr ymgeisydd
Manyleb y person
Knowledge and Experience
Meini prawf hanfodol
- Practical application experience in managing data science projects, from project design, method selection, control, optimisation, and implementation within the workplace. Including guidance, documentation, and leadership.
- Proven experience of line managing a team of analyst as part of a data science project.
- Knowledge and practical experience in designing algorithms including selection, using statistical and problem centric methods to design actionable outcome across a variety of data types.
- Proven practical experience and expertise in AI/machine learning methods using data including the implementation of statistical modelling and methods into a live production environment.
- Experience and strong proficiency in programming languages for data science, e.g., SQL, R and Python alongside the ability to use tools and packages such as Alteryx, Jupyter notebook, R Markdown, TensorFlow, Keras, Pytorch etc.
- Practical expertise in producing reproducible code and pipelines including documentation, governance and assurance frameworks, automation and code review using tools such as Git.
- Skilled in data visualisation, with expertise in employing best practices and a proven track record of effectively communicating statistical findings to stakeholders.
- Strong analytical and problem-solving skills, with the ability to analyse large and complex datasets, extract meaningful insight and actionable outcome to help inform business outcome.
- Practical experience in model development and production and the alignment with model governance, business, best practice, and regulatory standards.
Meini prawf dymunol
- Expertise and proven experience of fraud/anomaly detection.
- Extensive understanding of the NHS data landscape.
- Accredited Counter Fraud Specialist or member of Government Counter fraud Profession
Specialist Knowledge/Skills
Meini prawf hanfodol
- Proven experience in advanced data science techniques and statistical modelling methodologies within large and complex datasets.
- Practical and proven expertise of machine learning algorithms, including supervised, unsupervised, and semi supervised techniques used to build and deploy models.
- Significant expertise of techniques used to prepare data for model selection, such as sampling, normalisation, imputation including theoretical knowledge.
- Proven experience in operating within a big data environment, encompassing the management of substantial data volumes sourced from diverse origins, including data handling, pre-processing, scalability, storage optimisation, and the application of efficient processing methodologies.
- Proven experience in generating, analysing, and interpreting data within professional settings, demonstrating a deep understanding of data exploration, statistical theory and understanding of the results produced, statistical inference, and predictive modelling principles.
- Practical experience in critically assessing and explaining computational and mathematical outcomes, including model selection, evaluation. Demonstrating transparency and assurance processes are fully documented and justified.
- Practical experience in critically evaluating and explaining computational and mathematical results, encompassing model selection and evaluation. Demonstrating transparency and assurance processes are fully documented and justified.
- Practical expertise and expertise in a professional capacity with GDPR and privacy laws which significantly impact data science practices.
Meini prawf dymunol
- Knowledge of generative AI models involving NLP across both open and closed large language models.
- Domain knowledge of key NHSCFA thematic areas and corresponding datasets across the NHS, wider public sector and beyond.
- Practical expertise of machine learning algorithms used to detect fraudulent activity.
Qualifications
Meini prawf hanfodol
- Degree, in numerate subject or equivalent experience/learning developed in a similar role (e.g., Mathematical, Data Science, Computer Science, Physics, or related discipline). For the purposes of this job description equivalent experience would be management and technical experience in a similar sized and complex organisation, successfully providing the technical ability concerning data science alongside the necessary drive, leadership, and direction.
- Have worked in a statistical/data science field and are able to demonstrate continuous professional development.
Meini prawf dymunol
- Master’s degree in numerate subject or equivalent experience/learning developed in a similar role (e.g., Mathematical, Data Science, Computer Science, Physics, or related discipline).
Communication Skills
Meini prawf hanfodol
- Advanced written and verbal communication skills, including the presentation of complex information, writing, presenting, and other documentation to both internal and external stakeholders.
- Effectively communicate and articulate findings to stakeholders at all management levels, addressing challenges to results with, justification, clarity, and precision.
- Must be able to provide and receive highly complex, sensitive, or contentious information, negotiate with senior stakeholders on difficult and controversial issues, and present complex and sensitive information to influential groups.
- Can lead, manage, and motivate a team, including the devising and explanation of complex methodologies and methods.
Meini prawf dymunol
- Politically astute with knowledge of national and regional decision making and influencing bodies.
- Provided expert guidance on data science methodologies and results within the NHS.
Rhagor o fanylion / cyswllt ar gyfer ymweliadau anffurfiol
- Enw
- Susan Proctor
- Teitl y swydd
- Data Scientist SME Manager
- Cyfeiriad ebost
- [email protected]
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