Biomedical Statistician and Statistical Programmer

A. Statistical design, analysis and evaluation Lead the statistical design and analysis of complex evaluations and health and care studies, defining appropriate populations, outcomes, comparison groups, analytical approaches and sensitivity analyses. Apply advanced statistical methods, including regression, survival, longitudinal, time-series, causal inference, predictive modelling and simulation approaches where appropriate. Identify and address confounding, selection bias, clustering, missing data, measurement error and other sources of uncertainty. B. Statistical programming, data quality and reproducibility Develop and maintain robust statistical code and analytical pipelines using R, Python, SQL, Stata or equivalent tools. Work with data engineering and analytical colleagues to specify data requirements, define cohorts and variables, and assess the completeness, validity and fitness for purpose of linked health and care data. Apply reproducible analytical practices, including structured code, version control, testing, documentation, peer review and automated reporting. C. Modelling and decision support Develop, validate and interpret statistical, prognostic and predictive models, assessing their validity, calibration, stability, equity and operational usefulness. Provide statistical input to economic evaluations and decision models, including estimation of effects, parameters, uncertainty and outcomes. Ensure consistency between statistical evidence, model assumptions and the decisions being supported. D. Reporting, governance and assurance Develop proportionate statistical analysis plans and produce clear technical reports, methodological appendices, visualisations, automated outputs, committee papers and presentations. Establish and maintain statistical quality-assurance standards, including code review, validation of key outputs, review of assumptions and transparent documentation of methods, limitations and material departures from agreed analysis plans. E. Professional advice and communication Provide authoritative statistical advice to multidisciplinary colleagues and senior decision-makers. Explain methods, findings, uncertainty, limitations and generalisability clearly to technical and non-technical audiences. Provide constructive professional challenge where analytical approaches or conclusions may be misleading or unsupported. F. Leadership, capability and delivery Lead a portfolio of complex statistical work, managing priorities, risks, dependencies and delivery timescales in proportion to the importance and complexity of the decision. Provide professional leadership, supervision, peer review and coaching to analysts, statisticians and multidisciplinary colleagues. Develop reusable code, methods guidance, templates and training resources that strengthen statistical capability across the ICB. Evaluate emerging statistical methods and analytical technologies where these may improve quality or efficiency, ensuring appropriate governance, testing and professional oversight. Job description Job responsibilities A. Statistical design, analysis and evaluation Lead the statistical design and analysis of complex evaluations and health and care studies, defining appropriate populations, outcomes, comparison groups, analytical approaches and sensitivity analyses. Apply advanced statistical methods, including regression, survival, longitudinal, time-series, causal inference, predictive modelling and simulation approaches where appropriate. Identify and address confounding, selection bias, clustering, missing data, measurement error and other sources of uncertainty. B. Statistical programming, data quality and reproducibility Develop and maintain robust statistical code and analytical pipelines using R, Python, SQL, Stata or equivalent tools. Work with data engineering and analytical colleagues to specify data requirements, define cohorts and variables, and assess the completeness, validity and fitness for purpose of linked health and care data. Apply reproducible analytical practices, including structured code, version control, testing, documentation, peer review and automated reporting. C. Modelling and decision support Develop, validate and interpret statistical, prognostic and predictive models, assessing their validity, calibration, stability, equity and operational usefulness. Provide statistical input to economic evaluations and decision models, including estimation of effects, parameters, uncertainty and outcomes. Ensure consistency between statistical evidence, model assumptions and the decisions being supported. D. Reporting, governance and assurance Develop proportionate statistical analysis plans and produce clear technical reports, methodological appendices, visualisations, automated outputs, committee papers and presentations. Establish and maintain statistical quality-assurance standards, including code review, validation of key outputs, review of assumptions and transparent documentation of methods, limitations and material departures from agreed analysis plans. E. Professional advice and communication Provide authoritative statistical advice to multidisciplinary colleagues and senior decision-makers. Explain methods, findings, uncertainty, limitations and generalisability clearly to technical and non-technical audiences. Provide constructive professional challenge where analytical approaches or conclusions may be misleading or unsupported. F. Leadership, capability and delivery Lead a portfolio of complex statistical work, managing priorities, risks, dependencies and delivery timescales in proportion to the importance and complexity of the decision. Provide professional leadership, supervision, peer review and coaching to analysts, statisticians and multidisciplinary colleagues. Develop reusable code, methods guidance, templates and training resources that strengthen statistical capability across the ICB. Evaluate emerging statistical methods and analytical technologies where these may improve quality or efficiency, ensuring appropriate governance, testing and professional oversight.