Senior Statistician

About This Role

This senior statistician role supports a major public sector environmental research program, focusing on understanding long-term changes in biodiversity across monitoring sites in England. It offers the opportunity to apply advanced statistical methods to complex ecological datasets, helping to identify trends, understand drivers of biodiversity change, and inform future monitoring strategies.

Responsibilities

• Lead the statistical analysis of long-term ecological and environmental datasets to identify trends, relationships, and potential drivers of biodiversity change
• Investigate the effects of multiple environmental pressures, including climate change and air pollution, on ecosystems and biodiversity
• Apply appropriate statistical methods to assess relationships between environmental variables and ecological outcomes, accounting for uncertainty and limitations in the data
• Review existing monitoring and survey methodologies, identifying opportunities to improve statistical power, efficiency, and the quality of research outputs
• Contribute to the development and refinement of future survey and monitoring designs, retaining established methodologies where appropriate
• Evaluate alternative analytical approaches and recommend suitable statistical techniques based on the research objectives and available data
• Work collaboratively with researchers, environmental specialists, academic partners, and public sector stakeholders
• Produce clear statistical reports, technical documentation, and research outputs, communicating complex findings to both technical and non-technical audiences
• Manage analytical deliverables effectively, ensuring work is completed to agreed deadlines and quality standards

Requirements

• A degree in Statistics, Mathematics, Ecology, Environmental Science, or a related discipline, or equivalent relevant experience
• Strong experience applying statistical methods to complex datasets, ideally within environmental, ecological, or scientific research
• Practical experience using statistical software such as R, Python, or SPSS
• Strong understanding of statistical modeling, multivariate analysis, statistical inference, parametric and non-parametric methods, and uncertainty quantification
• Experience with experimental design, survey design, or the evaluation of monitoring methodologies
• Ability to interpret complex data, identify meaningful patterns, and communicate findings clearly
• Strong analytical, problem-solving, and critical-thinking skills
• Excellent written and verbal communication skills, with the ability to work collaboratively across multidisciplinary teams
• Proven ability to manage competing priorities and deliver high-quality work within agreed timescales