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Epidemiological Consultant

United Kingdom, Northampton · Job Posted May 29, 2026
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Job Description

The MND Association is in search for an experienced Epidemiological Consultant to support a national review of incidence and prevalence estimates for the association in the UK. In this high impact collaborative post, you will reconcile variation across key datasets, including the MND Register, Hospital Episode Statistics (HES)–derived datasets, and the MND Association’s internal database. You will work closely with the MND Register team at King’s College London and Oxford University, and be comfortable working with complex population health data in secure research environments. All work will be undertaken within the King's College London Trusted Research Environment (TRE), in line with governance and disclosure control requirements.

Job Responsibility

  • Conduct a detailed audit of multiple datasets to assess structure, completeness, and consistency
  • Identify and address differences in case definitions, coding practices, and inclusion criteria
  • Develop and document a harmonised analytical framework for comparing datasets
  • Define and standardise MND case definitions, including consideration of subtypes and uncertain diagnoses
  • Align cohorts across data sources to enable meaningful comparison
  • Design and apply approaches to identify duplication and overlap within and between datasets
  • Incorporate mortality and survival data to refine prevalence estimates
  • Assess the feasibility and application of capture–recapture or similar completeness methods
  • Produce sensitivity analyses and low, central, and high prevalence scenarios
  • Clearly document assumptions, limitations, and interpretation of findings
  • Deliver reproducible, well‑documented analytical code within the KCL Trusted Research Environment
  • Produce both technical reports and senior‑level summary outputs for key stakeholders

Requirements

  • Advanced expertise in epidemiology, biostatistics, or health data science
  • Strong experience applying advanced statistical methods, such as capture–recapture or similar approaches
  • Proven experience working within secure data environments (e.g. Trusted Research Environments, NHS Digital, ONS Secure Research Service)
  • Strong analytical programming skills, with proficiency in R and/or Python
  • Experience working with large, complex health or population‑level datasets
  • Ability to clearly document methodology and communicate findings to both technical and senior audiences
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