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- Tasks: Manage funding algorithms and quality assurance processes while delivering innovative funding mechanisms.
- Company: Join a leading research funding team at Research England.
- Benefits: Enjoy 30 days annual leave, flexible working, and a fantastic pension scheme.
- Why this job: Make a real impact in the Higher Education sector with your analytical skills.
- Qualifications: Degree in mathematics or equivalent experience, plus coding skills in R.
- Other info: Collaborative environment with opportunities for professional growth and development.
The predicted salary is between 43116 - 60462 £ per year.
2 weeks ago Be among the first 25 applicants
Salary : £43,116 Per AnnumHours: Full Time / Part Time (Minimum 0.8 FTE)Contract Type : Fixed Term (Concluding 31 December 2030)Location : Westward House, Stoke Gifford, BristolGrade : UKRI Band EPositions Available : 1Closing Date : Sunday 26 October 2025Proposed Interview Date : Mid-November 2025
Job OverviewThe research funding team is responsible for Research England\’s research funding policy evaluation, development, and implementation. 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.
Key Responsibilities
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 (also referred to as formula‐based) 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 their 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 self and/or team.
Broader Sector Analysis
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 stakeholders.
Quality Assurance Support
Ensure that the team QA process is always followed.
Make sure that QA approvals are escalated as necessary.
Other
Develop and lead relationships with internal and external stakeholders 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.
Person Specification
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 data quality issues may have arisen.
Experience of quality assurance processes, and an awareness of how these minimise errors in analytical outputs.
Able to lead developing work areas, with responsibility for high‐profile processes or projects.
Strong written and verbal communication skills, to explain complex techniques and results to non‐experts, including clarifying policy questions and guiding colleagues in interpretation of data.
Able to plan and manage a varied and demanding workload and respond to changing priorities.
Benefits
An outstanding defined benefit pension scheme.
30 days\’ annual leave in addition to 10.5 public and privilege days (full time equivalent).
Employee discounts and offers on retail and leisure activities.
Employee assistance programme, providing confidential help and advice.
Flexible working options.
UKRI is an Equal Opportunity & Disability Confident Employer.
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Senior Statistical Analyst employer: Innovate UK
Contact Detail:
Innovate UK Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Statistical Analyst
✨Tip Number 1
Network like a pro! Reach out to current or former employees in similar roles on LinkedIn. A friendly chat can give you insider info and might even lead to a referral.
✨Tip Number 2
Prepare for the interview by practising common questions related to statistical analysis and coding. We recommend using mock interviews with friends or online platforms to boost your confidence.
✨Tip Number 3
Showcase your skills! Bring along examples of your previous work, especially any projects involving R coding or data analysis. This will help you stand out and demonstrate your expertise.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed and shows you’re serious about joining our team.
We think you need these skills to ace Senior Statistical Analyst
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Senior Statistical Analyst role. Highlight your experience with coding, data analysis, and quality assurance processes, as these are key aspects of the job.
Showcase Your Skills: Don’t just list your skills; demonstrate them! Use specific examples from your past work that showcase your analytical abilities and how you've successfully tackled complex problems using quantitative methods.
Be Clear and Concise: When writing your application, keep it clear and to the point. Avoid jargon where possible and make sure your communication is easy to understand, especially since you'll be explaining complex techniques to non-experts.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets seen by the right people and helps us keep track of all applicants efficiently.
How to prepare for a job interview at Innovate UK
✨Know Your Numbers
As a Senior Statistical Analyst, you'll need to demonstrate your analytical prowess. Brush up on key statistical concepts and be ready to discuss how you've applied quantitative methods in past projects. Prepare examples that showcase your coding skills, especially in R, and how you've manipulated complex datasets.
✨Understand the Funding Landscape
Familiarise yourself with the Higher Education sector and the funding mechanisms used by Research England. Be prepared to discuss how you would approach developing and implementing funding models. Showing that you understand the context of your work will impress your interviewers.
✨Quality Assurance is Key
Since quality assurance processes are crucial for this role, think of specific instances where you've implemented QA measures in your previous work. Be ready to explain how these processes helped minimise errors and improve the reliability of your analytical outputs.
✨Communicate Clearly
You'll need to present complex data findings to non-experts, so practice explaining your work in simple terms. Think about how you can convey your analytical insights effectively, and prepare to discuss how you've guided colleagues in interpreting data in the past.
