Senior Statistical Analyst

To support the algorithm-driven allocation of research funds to higher education providers by:

• developing a sound knowledge of the Higher Education sector data used by the analyst team to underpin SIRF funding allocations.
• developing a sound knowledge of the algorithm-driven (also referred to as formula-based) research funding methods used by Research England and collaborating with colleagues to develop, maintain and implement funding models.
• proactively engaging early and often within and across teams to share analytical results, and to discuss findings, taking data limitations into account.
• using the most appropriate methods and tools to determine and apply appropriate quantitative methods and write code to model formula-driven funding allocations, confidently adapting their approach as required to effectively manipulate a variety of data sources.
• engaging with quality assurance and publication processes for disseminating outcomes.
• taking responsibility for the presentation of high-quality statistical advice and analysis undertaken by self and/or team.

• supporting the development of analytical models that underpin sector benchmarking activities.
• investigating available datasets and determining possible metrics for use in sector analysis.
• collaborating with colleagues on possible ways to gain sector insight from datasets and how these could feed data visualisations that may aid in supplying this insight to internal and external partners.

• ensuring that the team QA process is always followed.
• making sure that QA approvals are raised as necessary.

• developing and leading relationships with internal and external partners where pertinent, potentially including representing RE at meetings and events.
• delegating tasks to colleagues and supporting junior staff as required.
• supporting wider areas of the Research Funding team activities as they arise.

• Degree in mathematics, or another subject - or equivalent professional experience - containing formal statistical training. (S)
• Experience of coding in R language to manipulate large complex datasets. (S/I)
• Strong analytical experience and knowledge of applied quantitative methods, including a track record in the practical application of analysis or research with an in-depth knowledge of the development of computational and statistical methods for solving complex problems and presenting analytical findings. (S/I)
• Proven ability to critically assess data, recognise drivers for change, and identify where data quality issues may have arisen. (S/I)
• Experience of quality assurance processes, and an awareness of how these minimise errors in analytical outputs. (S/I)
• Able to lead developing work areas, with responsibility for high-profile processes or projects. (S/I)
• Strong written and verbal communication skills, to explain sophisticated techniques and results to non-experts, including clarifying policy questions and guiding colleagues in interpretation of data. (S/I)
• Able to plan and manage a varied and demanding workload and respond to changing priorities. (I)

Demonstrates expertise in statistical analysis and quantitative methods, with proficiency in R programming for data manipulation and model development. Capable of leading projects, ensuring quality assurance, and effectively communicating complex findings to diverse audiences.

• R Programming
• Statistical Analysis
• Quantitative Methods
• Quality Assurance Processes
• Data Communication

• Statistical Training
• Data Manipulation
• Analytical Modeling
• Computational Methods
• Data Quality Assessment

• Written Communication
• Verbal Communication
• Workload Management
• Team Collaboration
• Leadership

• Degree in Mathematics

• Higher Education Sector
• Research Funding
• Data Visualization
• Benchmarking Activities
• Analytical Results