Senior Statistical Analyst

Contract Type:
Fixed Term until 31 December 2030

Hours:
Full Time (Part Time min. 0.8 FTE)

Closing Date: 16th August 2026

Are you looking for a new challenge? An opportunity to work on a major, high-profile, project in a close-knit team and a fast-paced environment? Do you want to use your knowledge and skills in an agile way across a broad range of focus areas, learning new things and innovating along the way? Do you want to work with some of the most highly respected people across the UK’s research community?

Then this may be the opportunity for you!

As a Senior Statistical Analyst at Research England working across the Research and Analysis Directorates, you will be in a key position to design and deliver analytical tools and processes that directly support the allocation of £2 billion of strategic institutional research funding (SIRF) to higher education providers in England.

Working primarily with key analysts and the Research Data & Evidence team, you will be responsible for managing work areas from the design and development of funding algorithms and quality assurance processes to delivery of new funding mechanisms and be committed to innovation and continuous improvement.

A sound understanding of an analytical product development cycle is crucial for this role, including significant experience of coding, analysing data (including applied quantitative methods), and presenting findings. Equally important is experience of quality assurance processes, and an awareness of how these minimise errors in analytical outputs. You will be well‑organised and self‑motivated, ideally with a background in statistical or financial modelling.

Capable of working to your own initiative, you will contribute substantially to the development and delivery of projects and be a confident communicator with people at all levels.

• Develop a sound knowledge of the Higher Education sector data used by the analyst team to underpin SIRF funding allocations.
• Develop a sound knowledge of the algorithm‑driven research funding methods used by Research England and collaborate with colleagues to develop, maintain and implement funding models.
• Proactively engage early and often within and across teams to share analytical results, and to discuss findings, taking data limitations into account.
• Use the most appropriate methods and tools to determine and apply appropriate quantitative methods and write code to model formula‑driven funding allocations, confidently adapting your approach as required to effectively manipulate a variety of data sources.
• Engage with quality assurance and publication processes for disseminating outcomes.
• Take responsibility for the presentation of high‑quality statistical advice and analysis undertaken by you and/or your team.
• Support the development of analytical models that underpin sector benchmarking activities.
• Investigate available datasets and determine possible metrics for use in sector analysis.
• Collaborate 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.
• Ensure that the team QA process is always followed.
• Make sure that QA approvals are raised as necessary.
• Develop and lead relationships with internal and external partners where pertinent, potentially including representing RE at meetings and events.
• Delegate tasks to colleagues and support junior staff as required.
• Support 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.
• Experience of coding in R language to manipulate large complex datasets.
• 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.
• Proven ability to critically assess data, recognise drivers for change, and identify where…