Senior Research Information Analyst
University of Cambridge
Job Overview
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Job Description
Senior Research Information Analyst at University of Cambridge
This post is fixed-term until 9 November 2026 or the return of the post holder, whichever is the earlier. Are you interested in analysing data to help shape conversations, policies, and procedures? Do you like to work with people to understand their problems and think up solutions? Do you thrive on the opportunity to learn new ways of working in a fast-paced environment, and promote continuous improvement?
The University of Cambridge Research Office wishes to appoint a Senior Research Information Analyst to support the University's submission for the Research Excellence Framework (REF) 2029. This is an excellent opportunity to join the University of Cambridge Research Information team at an exciting time and in a dynamic role.
Candidate Profile
Candidates should be able to demonstrate excellent organisational, communication and interpersonal skills, including the ability to liaise credibly with academic colleagues, researchers, and external contacts. You will be well-organised, proactive, and responsive, with the ability to communicate complex data at all levels and to work well within and across teams. With an excellent level of attention to detail, you will be able to prioritise effectively and work quickly and accurately. You will be flexible and adaptable, with excellent problem-solving skills and the ability to remain calm under pressure whilst managing workloads to meet multiple deadlines.
Key Responsibilities
The post holder will work with professional services colleagues to support and maintain the University's research information in preparation for REF2029. This will involve building capacity and technical solutions for analysis reporting, and supporting their effective implementation across the University. Acquiring a thorough understanding of the way research and researchers work in a variety of disciplines is essential to performing the role well.
A problem-solving attitude and an eye for opportunities to work more effectively would be highly valuable assets in this role. You will need a good understanding of the way databases work, and some understanding or experience of data connectors, APIs, statistics, and Extract, Transform and Load (ETL) processes in R or Python would be great additional skills to bring to the team. Because of the nature of the questions we face as a team, the role will necessarily involve a lot of data curation and preparation in order to be able to provide analyses and then to be able to describe both the insight they provide and, ultimately, their limitations.
Work Arrangement
The University is supportive of hybrid working, where the majority of work is undertaken on University premises and some in a remote working environment. This role requires onsite attendance for two days a week.
Application Process
When applying, please upload your CV and cover letter which clearly sets out how you meet the criteria listed in the person specification along with relevant examples. Candidates will be shortlisted and invited to interview based upon these criteria. Please include details of your referees, including email address and phone number, one of which must be your most recent line manager. If you have questions about the application process, contact CROrecruitment@admin.cam.ac.uk. Please quote reference EW48957 on your application and in any correspondence about this vacancy.
Key skills/competency
- Data analysis
- Research Excellence Framework (REF)
- ETL processes (R/Python)
- Database understanding
- APIs and data connectors
- Data curation
- Problem-solving
- Communication skills
- Organisational skills
- Stakeholder engagement
How to Get Hired at University of Cambridge
- Research University of Cambridge's culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor.
- Tailor your application: Customize your CV and cover letter, highlighting experience in data analysis, REF, and communication.
- Showcase problem-solving skills: Provide specific examples of how you've tackled complex data challenges and improved processes.
- Demonstrate technical aptitude: Emphasize any experience with databases, ETL processes (R/Python), APIs, and data connectors.
- Prepare for a values-based interview: Understand how your skills align with the University's commitment to research excellence and collaborative work.
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