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Data analyst in health data science and innovation

Edinburgh
The University of Edinburgh
Data analyst
Posted: 7 October
Offer description

Overview

Grade UE07: £41,064 to £48,822 per annum

CMVM / School of Population Health Sciences / Usher Institute

Full time: 35 Hours per week

Fixed Term: November 2025 to 31st October 2027

Location: Usher Institute, Edinburgh Bioquarter

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 Data Analyst 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.

You will work in a fast-paced, agile research environment where data meets decision-making. Working in close partnership with senior health care and community care leaders, you will help 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 and higher degree in a relevant subject, or able to demonstrate relevant and equivalent experience
* Experience in using statistical programming for data analysis and machine learning/AI, using python or R
* The ability to extract data and a good understanding of transferring and transforming data, for analysis using different statistical packages.
* Good archiving, data management, and organizational skills
* Ability to work well within a multi-disciplinary team


Application documents

* CV
* Cover letter


As a valued member of our team you can expect

• A competitive salary.
• An exciting, positive, creative, challenging and rewarding place to work. We give you support, nurture your talent and reward success.
• To be part of a diverse and vibrant international community.
• Comprehensive Staff Benefits, which includes a generous holiday entitlement, a defined benefits pension scheme, staff discounts, family-friendly initiatives, flexible working and much more. The University’s staff benefits page provides further information.


Championing equality, diversity and inclusion

The University of Edinburgh holds a Silver Athena SWAN award in recognition of our commitment to advance gender equality in higher education. We are members of the Race Equality Charter and we are also Stonewall Scotland Diversity Champions, actively promoting LGBT+ equality.

Prior to any employment commencing with the University you will be required to evidence your right to work in the UK. Further information is available on the right to work webpages.

On this occasion the University will not consider applicants requiring sponsorship for this role. International workers will therefore only be able to take up this role if they can demonstrate an alternative right to work in the UK.


Key dates

The closing date for applications is 9th October 2025.

Unless stated otherwise the closing time for applications is 11:59pm GMT. If you are applying outside the UK the closing time on our adverts automatically adjusts to your browser’s local time zone.

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