Research Fellow -Health Data Science Full-time NEW

Research Fellow -Health Data Science

The National Institute for Health and Care Research (NIHR) Inequalities Challenge: Maternity Disparities Consortium (MDC) is a £50million programme bringing together a diverse range of organisations with expertise in research, capacity building and working with people/communities to deliver a step change in maternity and neonatal outcomes and the inequalities in them.

One of the three Themes within the MDC focuses on preconception and interpregnancy health. A partnership between the University of Southampton, Queen's University Belfast and University College London is leading this research into health before and between pregnancies, working with Hampshire and Isle of Wight and Central East Integrated Care Boards, Northern Ireland Health Trusts and public contributors.

The UCL EGA Institute for Women's Health, mission to deliver excellence and innovation to make a real and sustainable difference to women's and babies' health locally, nationally and worldwide, is part of the MDC.

About the role

We are seeking a collaborative, experienced and self-motivated individual for an exciting opportunity to evaluate the implementation of preconception and interpregnancy interventions using routine data. The postholder will have a strong foundation in health data science, statistical analysis and epidemiological approaches, data linkage and governance, and experience of analysing longitudinal data. They will join the data workstream of the preconception and interpregnancy theme and link with the cross-cutting data workstream of the MDC.

Role is 1.0FTE, with an end date of 31 May 2028. Aspects of the research may continue beyond May 2028, subject to NIHR review. UCL Grade 7 (£46,261- £54,019 inc London Weighing) is dependent upon having been awarded a PhD; if this is not the case, initial appointment will be at Grade 6B spine point with payment at Grade 7 being backdated to the date of final submission of the PhD Thesis.

This role is eligible for UCL Sponsorship.

This appointment is subject to UCL Terms and Conditions of Service for Research and Professional Services Staff. Please visit https://www.ucl.ac.uk/human-resources/conditions-service-research-teaching-and-professional-services-staff.

About you

An experienced research fellow with a strong background in health data science, epidemiology, or a related field, with experience analysing complex longitudinal and routinely collected health data. You should be confident applying statistical methods, working with linked datasets, and navigating data governance requirements. Strong programming skills (e.g. R, Stata, or Python) are essential. You will be organised and analytical, able to work independently and collaboratively within multidisciplinary teams and ready to take. Excellent communication skills are required to present findings to academic, clinical, and policy audiences. Experience in applied health research, particularly in maternal or population health, is desirable.

What we offer

As well as the exciting opportunities this role presents, we also offer great benefits. Visit https://www.ucl.ac.uk/work-at-ucl/rewards-and-benefits for more information.

Our commitment to Equality, Diversity and Inclusion

As London's Global University, we know diversity fosters creativity and innovation, and we want our community to represent the diversity of the world's talent. We are committed to equality of opportunity, to being fair and inclusive, and to being a place where we all belong. We particularly encourage applications from candidates who are likely to be underrepresented in UCL's workforce. These include people from Black, Asian and ethnic minority backgrounds; disabled people; LGBTQI+ people; and for our Grade 9 and 10 roles, women. More on our commitment to Equality, Diversity and Inclusion here:

https://www.ucl.ac.uk/equality-diversity-inclusion/

IfWH holds an Athena SWAN Gold award, in recognition of our long-term commitment and 'beacon' status in advancing gender equality.

Available documents

• download: Research Fellow JD.pdf