Research Assistant in Health Data Science and Innovation

Full time: 35 hours per week

Fixed term: available from 1^st November 2025 to 31^st October 2027

Location: Usher Institute, Edinburgh Bioquarter, EH16 4UX

We will also consider requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular (weekly) on-campus working. The Usher Institute expects a minimum of 40% on campus working.

The Centre for Population Health Sciences at the Usher Institute within The University of Edinburgh is looking for an experienced and enthusiastic Research Assistant in Health Data Science and Innovation.

The Opportunity:

This post offers a rare and exciting opportunity to help shape the future of unscheduled and emergency health care in Scotland by harnessing the power of advanced analytics and real-world health data.  

As a Research Assistant, you will work in a fast-paced, supportive research environment where data meets decision-making. Supported by senior colleagues, you will contribute to a collaborative team that is working in close partnership with health and community care leaders. Your role will focus on helping to develop and implement data-driven approaches that generate actionable insights. These insights will drive rapid cycles of service improvement and directly inform frontline health care. This work is fully aligned with Scotland’s Health and Social Care Data Strategy and will contribute to a more responsive, efficient, and equitable health system.

This programme will go beyond data analysis to enable meaningful system-wide transformation. From enhancing urgent care pathways to strengthening community prevention strategies, your contributions will make a real-world impact through data, helping to build a more adaptive health and care system that can meet rising demand with precision and compassion.

Informal enquiries may be directed to Professor Nazir Lone, Professor of Critical Care and Epidemiology (nazir.lone@ed.ac.uk)

Your skills and attributes for success:

• A good undergraduate degree in a relevant subject, or able to demonstrate relevant and equivalent experience
• Proficiency in at least one statistical programming language (e.g. R, Python) 
• Ability to apply relevant knowledge, skills and techniques in research projects 
• High level of computer fluency with the ability and willingness to quickly become familiar with new applications when required
• Ability to work well within a multi-disciplinary team

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