Senior Data Scientist

NHS Jobs; Ely; On-site; GBP 60,000 - 90,000; Full time

Job summary

NHS Jobs is seeking professionals for advanced analytics and modelling to independently lead complex analytic projects and provide technical advice on methods and interpretation. The role covers strategic commissioning, population health analytics and the development of reproducible analytical assets.

You will work with commissioners and clinicians to translate findings into actionable recommendations, ensure quality assurance, and contribute to the wider data science programme within the

Responsibilities

• A. Independently lead complex analytical projects using statistical, data science and OR methods.
• B. Lead advanced analysis of population need, inequalities, demand, utilisation, capacity and outcomes for strategic planning.
• C. Develop, validate and implement analytical products with clear use, assumptions and limitations.
• D. Translate findings into clear, actionable recommendations for commissioners and clinicians.
• E. Undertake peer review, supervise analysts and promote reproducibility and continuous learning.

Job description

A. Advanced analytics and modelling

Independently lead complex analytical projects using appropriate statistical, data science and operational research methods. Select proportionate methods that reflect the decision context, data quality, implementation requirements and limitations of the evidence. Provide technical advice on analytical methods and interpretation within own areas of expertise.

B. Strategic commissioning, population health and evaluation analytics

Lead advanced analysis of population need, inequalities, demand, utilisation, capacity, outcomes and variation to support strategic planning and commissioning. Apply analytical methods to option appraisal, population segmentation, targeting, pathway redesign and neighbourhood-level decision-making. Lead defined data science components of evaluations and contribute to multidisciplinary evaluation of services, programmes and interventions. Integrate quantitative findings with clinical, public health, economic, operational and qualitative evidence.

C. Analytical products, data and quality assurance

Develop, validate and implement analytical products supporting strategic and operational decisions, clearly defining their intended use, population, outcomes, assumptions and limitations. Apply proportionate validation, sensitivity analysis, peer review and documentation; assess performance and potential bias across population groups. Work with large, complex and linked health and care datasets, assessing quality, provenance, linkage and fitness for purpose and working with relevant colleagues to secure authorised access. Use R and/or Python, SQL, version control, structured code, testing and automated reporting to produce transparent and reproducible analysis. Support monitoring, review and recalibration of operational products, escalating material concerns and contributing reusable code, templates and analytical assets.

D. Insight, communication and implementation

Work with commissioners, clinicians and other decision-makers to define analytical questions and translate findings into clear, actionable recommendations. Communicate methods, assumptions, uncertainty and limitations clearly to technical and non-technical audiences, particularly where analysis may influence significant service or resource decisions. Provide constructive technical challenge where methods or interpretations are unsupported, and support stakeholders to implement analytical products and assess their benefit.

E. Professional contribution and project delivery

Undertake peer review and quality assurance in line with standards established by the Lead Data Scientist. Provide technical supervision, coaching and support to analytical colleagues and contribute to shared methods, standards and resources. Manage priorities, risks and dependencies within assigned projects and support delivery of the wider data science work programme. Promote reproducibility, transparency, inclusion, methodological rigour and continuous learning.