Lead Data Scientist
| Company: | NHS Jobs |
|---|---|
| Salary: | £66,582 to £77,368 a year |
| Hours: | Full-time |
| Location: | Ely, CB7 4EA |
| Job type: | Permanent |
| Posting date: | 23 Oct 2026 |
| Closing date: | 8 Nov 2026 |
Summary
A. Professional leadership and strategy Lead the data science discipline across the integrated Strategic Value Unit and Population Health Unit, providing authoritative professional advice and setting the direction for its development. Work with the Head of Evaluation and other professional leads to align the analytical work programme and data science capability with ICB priorities. Identify where data science can materially improve decisions and advocate proportionate approaches based on the question, available evidence and intended use. B. Advanced analytics and modelling Lead complex analytical projects using appropriate statistical, data science and operational research methods. Apply techniques such as predictive modelling, machine learning, forecasting, segmentation, optimisation, simulation, systems modelling and scenario analysis where they add value. Ensure methods reflect the decision context, data quality, implementation requirements and limitations of the evidence. C. 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 the data science components of evaluations and contribute multidisciplinary evaluation design and delivery of services, programmes and interventions, assessing impact, effectiveness, equity and implementation. Integrate quantitative findings with clinical, public health, economic, operational and qualitative evidence. D. Analytical products, assurance and governance Lead the development, validation and implementation of analytical products supporting strategic and operational decisions, including risk stratification, case-finding and population segmentation tools. Define each products intended use, population, outcomes, assumptions and limitations, with proportionate arrangements for validation, sensitivity analysis, independent review and documentation. Assess performance and potential bias across population groups and ensure operational products are monitored, reviewed, recalibrated and retired where appropriate. E. Data and analytical engineering Provide senior technical oversight of large, complex and linked health and care datasets, including their provenance, coding, completeness, linkage quality, limitations and fitness for purpose. Work with data engineering, architecture and information governance colleagues to define requirements and secure reliable, authorised access to analytical data. Lead reproducible analytical practice using R and/or Python, SQL, version control, structured code, testing, peer review and automated reporting. F. Insight, influence and implementation Work with decision-makers to define analytical questions and translate findings into clear recommendations for strategy, commissioning and delivery. Communicate assumptions, uncertainty and limitations clearly, particularly where analysis may influence significant service or resource decisions. Provide constructive challenge where methods, assumptions or interpretations are unsupported or potentially misleading. Support stakeholders to implement analytical products and assess whether they continue to deliver their intended benefit. G. Team and portfolio leadership Line manage the Senior Data Scientist, Data Scientist and Clinical Policy Data Analyst, including objectives, appraisal, performance, development and wellbeing. Provide technical supervision, coaching and professional development across the analytical function, fostering curiosity, constructive challenge, inclusion and continuous learning. Lead a portfolio of complex analytical work, balancing priorities, risks, dependencies, resources and timescales to deliver high-quality outputs.