Senior Research Statistician

Title: Senior Research Statistician Location: Remote, United Kingdom

We are growing and seeking a Senior Research Scientist (Statistician) to join our Evidence Synthesis and Decision Modelling team. You will collaborate with a highly specialized team of statisticians, health economists, and researchers on global projects supporting evidence generation, comparative effectiveness research, health technology assessment (HTA), and decision-making for pharmaceutical and biotechnology clients.

About You

Experienced Quantitative Researcher: You have a background in statistics, biostatistics, epidemiology, health economics, data science, or a related quantitative discipline, along with experience applying statistical methods in healthcare, life sciences, or outcomes research.

Evidence Synthesis and Statistical Methods Experience: You have experience planning, conducting, and reporting analyses that support evidence synthesis and comparative effectiveness research. You can critically assess clinical evidence, evaluate methodological assumptions, identify evidence gaps, and apply appropriate analytical methods to address complex research questions. Experience with indirect treatment comparison methodologies and analyses involving aggregate and/or individual patient-level data is desirable.

Technical Proficiency: You demonstrate strong statistical programming skills, particularly in R, and experience developing reproducible analytical workflows. You are comfortable working with complex datasets and applying a range of statistical methodologies appropriate to the research question.

Deliverable Creation: You contribute to client-ready deliverables including analysis plans, technical reports, presentations, manuscripts, and conference materials, and can communicate findings to both technical and non-technical audiences.

Key Responsibilities

• Lead and deliver statistical analyses supporting evidence generation, comparative effectiveness research, and healthcare decision-making projects.

• Design and implement fit-for-purpose analytical approaches that address client objectives and research questions.

• Conduct statistical analyses and develop reproducible analytical workflows using R.

• Lead analytical workstreams and contribute to project planning, delivery, and quality assurance activities.

• Interpret and communicate complex analytical findings to technical and non-technical audiences.

• Support HTA, reimbursement, market access, and Joint Clinical Assessment evidence-generation activities.

• Mentor junior team members and contribute to methodological best practices.

• Contribute to scientific publications, conference presentations, proposals, and thought leadership activities.

Required Experience and Competencies

• Master's degree or PhD in Statistics, Biostatistics, Epidemiology, Health Economics, Data Science, or a related quantitative discipline.

• Approximately 4 years or more of relevant professional experience.

• Strong R programming skills and experience conducting statistical analyses.

• Experience applying statistical methods to healthcare, outcomes research, evidence generation, or related research areas.

• Experience with evidence synthesis and comparative effectiveness research.

• Strong analytical reasoning and problem-solving skills.

• Ability to communicate complex findings clearly and effectively.

• Ability to work independently and as part of a multidisciplinary team.

Helpful Experience and Competencies

• Knowledge of indirect treatment comparison methodologies such as network meta-analysis (NMA), matching-adjusted indirect comparison (MAIC), simulated treatment comparison (STC), or similar approaches.

• Experience with external control arm (ECA) analyses

• Experience with survival analysis, time-to-event modelling, or utility and health-related quality of life analyses.

• Experience working with individual patient level data.

• Understanding of Bayesian and/or frequentist statistical methods.

• Experience supporting HTA, reimbursement, market access, or evidence-generation activities.

• Familiarity with Python, or other programming languages.

• Experience contributing to scientific publications or conference presentations.

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