Senior Research Statistician

Role overview Lead statistical research that helps generate clinical evidence and inform healthcare decisions. You will work with statisticians, health economists, and researchers on evidence synthesis, comparative effectiveness, and health technology assessment projects, translating complex analyses into clear scientific and client-facing outputs.

Responsibilities - Lead statistical analyses for evidence generation, treatment comparisons, and healthcare decision-making. - Design analytical approaches suited to research questions and project objectives; assess evidence quality, assumptions, and gaps. - Build reproducible analysis workflows in R and work with complex clinical or outcomes datasets. - Lead analytical workstreams and contribute to project planning, delivery, and quality review. - Explain findings to both technical and non-technical audiences and support assessment, reimbursement, and market access evidence needs. - Mentor colleagues and contribute to publications, conference materials, proposals, and methodological guidance.

Requirements - Master’s degree or doctorate in statistics, biostatistics, epidemiology, health economics, data science, or a related quantitative field. - Around four or more years of relevant professional experience. - Strong R programming and applied statistical analysis skills, including reproducible workflow development. - Experience applying quantitative methods in healthcare, outcomes research, or related evidence-generation work. - Experience planning, conducting, and reporting evidence synthesis or comparative effectiveness analyses. - Strong analytical reasoning, clear communication, and ability to work independently and with multidisciplinary teams.

Nice to have - Indirect treatment comparisons such as network meta-analysis, matching-adjusted indirect comparison, or simulated treatment comparison. - External control arms, survival or time-to-event analysis, health utility or quality-of-life analysis, or individual patient-level data. - Familiarity with Bayesian and frequentist methods, Python, health technology assessment, reimbursement, or scientific publishing.