Data Science and AI Specialist

To provide advanced analytical, epidemiological, and data‑science support for research projects using NHS data hosted within the Trusted Research Environment (TRE). The postholder will work closely with investigators from NHS Greater Glasgow and Clyde (NHSGGC), the University of Glasgow (UofG), and industry partners to translate research ideas into robust analytical plans, ensure data are appropriately specified and prepared for analysis, and deliver high‑quality, reproducible outputs. The role focuses on real‑world health data analysis — including study design, data wrangling, phenotype development, data integration, and statistical and machine‑learning methods — to accelerate project delivery, strengthen grant applications, and advance the overall research capability of the TRE.

1. 
   
2. Support principal investigators by designing and implementing robust analytical and statistical workflows for complex clinical and population health datasets hosted in the TRE — including data wrangling, quality assessment, phenotype development, and exploratory analyses.
   
3. Develop reproducible and transparent analytical pipelines, ensuring data provenance, version control, and adherence to ethical and governance standards.
   
4. Working closely with clinicians, researchers, and data engineers across NHS and UofG to define project data requirements, optimise analytical design, and translate research questions into executable analyses.
   
5. Lead on technical aspects of data integration, statistical and machine‑learning model development, validation, interpretability, and deployment within the secure TRE environment.
   
6. Ensure all research activities comply with NHS data governance, ISO standards, and the TRE’s ethical frameworks.
   
7. Contribute to demonstration and exemplar projects (e.g., multimodal data integration, digital phenotyping, predictive analytics) that highlight the TRE’s analytical and AI capabilities.
   
8. Act as liaison between NHS Safe Haven, academic researchers, and University Services (e.g., Information Services, Centre for Data Science and AI) advising on data specifications, study design, and appropriate analytical methodologies.
   
9. Support the training and mentoring of researchers and students in applied health data science, statistical methods, and TRE workflows.
   
10. Perform administrative and governance‑related tasks relevant to TRE operations, including documentation, data access tracking, and project coordination.
    
11. Keep up to date with current knowledge and recent advances in the field / discipline.
    
12. Contribute to research outputs, grant applications, and dissemination activities that strengthen TRE capabilities and support collaborative funding bids.
    
13. Participate and engage with national and cross‑institutional AI/TRE initiatives and networks as appropriate.
    
14. Undertake any other reasonable duties as required by the Head of School / Director of Clinical TRE.
    
15. Contribute to the enhancement of the University’s international profile in line with the University Strategy.
    

• 
  
• A1 Scottish Credit and Qualification Framework level 12 (PhD) in a relevant discipline such as Epidemiology, Biostatistics, Health Data Science, or Health Informatics.
  
• A2 Strong knowledge of epidemiological and biostatistical principles applied to healthcare data, with experience integrating these with data‑science or AI/ML methods.
  
• A3 Demonstrable understanding of data governance and regulatory requirements for clinical data, including anonymisation, secure data handling protocols and workflows underpinning Trusted Research Environments (TREs).
  
• A4 Understanding of study design, phenotype development, and data quality assessment in real‑world healthcare research.
  

• 
  
• B1 Additional formal training or certification in Epidemiology, Biostatistics, Health Informatics, or Applied AI in Healthcare.
  
• B2 Knowledge of data standards and interoperability frameworks (e.g., OMOP, FHIR, SNOMED CT, ICD‑10) relevant to real‑world data integration.
  
• B3 Understanding of computable phenotypes, data harmonisation, or ontology development for clinical research.
  
• B4 Awareness of federated analytics, privacy‑preserving computation, or distributed learning within Trusted Research Environments.
  

• 
  
• C1 Proficiency in R and/or Python, with strong skills in health data wrangling, cleaning, integration, and visualisation; experience with analytical and machine‑learning frameworks (e.g., TensorFlow, PyTorch, Scikit‑learn).
  
• C2 Ability to manipulate, analyse, and interpret large or complex healthcare datasets within secure computing environments, ensuring reproducibility and integrity.
  
• C3 Excellent communication and interpersonal skills to work across interdisciplinary teams in both academic and clinical environments.
  
• C4 Proven ability to explain analytical findings and complex technical concepts to non‑specialist stakeholders, including clinicians, policymakers, and industry partners.
  
• C5 Problem‑solving mindset with the ability to work independently and manage multiple priorities.
  

• 
  
• D1 Experience in developing reproducible analysis pipelines using tools such as Git, Docker, or workflow managers.
  
• D2 Strong skills in data visualisation and dashboarding (e.g., R Shiny, Plotly, Dash, Power BI) for communicating insights to clinical and policy audiences.
  
• D3 Familiarity with advanced analytical techniques, such as causal inference, predictive modelling, or survival analysis in health data contexts.
  

• 
  
• E1 Significant experience in applied health data analysis — including study design, data specification, data wrangling, statistical analysis, and (where appropriate) machine‑learning model development or evaluation.
  
• E2 Experience working with sensitive health or clinical datasets within secure research environments or safe havens.
  
• E3 Experience contributing to research publications, technical reports, or grant‑funded projects through provision of analytical and methodological expertise.
  
• E4 Experience working within data governance and ethical frameworks, ideally in healthcare or public sector research.
  
• E5 Proven commitment to supporting the career development of colleagues and to other forms of collegiality appropriate to the career stage.
  

• 
  
• F1 Prior experience supporting Safe Haven/TRE governance committees, data access processes, or technical advisory groups.
  
• F2 Contribution to open‑source tools, data models, or methods for healthcare analytics or AI reproducibility.
  
• F3 Experience in preparing grant applications or preliminary data analyses that directly supported successful research funding.
  
• F4 Evidence of continuous professional development in health data science, AI ethics, or digital health innovation.
  

Informal enquiries should be directed toProfessor Sandosh Padmanabhan,

Previous applicants should not re‑apply for this position.

Salary will be Grade 7, £41,064 – £46,049 per annum.

This post is full time (35 hours p/w) and has funding for up to 3 years initially.

Relocation assistance will be provided where appropriate.

As a valued member of our team, you can expect:

1. 
   
2. A warm welcoming and engaging organisational culture, where your talents are developed and nurtured, and success is celebrated and shared.
   
3. An excellent employment package with generous terms and conditions including 41 days of leave for full‑time staff, pension – pensions handbook https://www.gla.ac.uk/myglasgow/payandpensions/pensions/, benefits and discount packages.
   
4. A flexible approach to working.
   
5. A commitment to support your health and wellbeing, including a free 6‑month UofG Sport membership for all new staff joining the University https://www.gla.ac.uk/myglasgow/staff/healthwellbeing/.
   

We believe that we can only reach our full potential through the talents of all. Equality, diversity and inclusion are at the heart of our values. Applications are particularly welcome from across our communities and in particular people from the Black, Asian and Minority Ethnic (BAME) community, and other protected characteristics who are under‑represented within the University. Read more on how the University promotes and embeds all aspects of equality and diversity within our community https://www.gla.ac.uk/myglasgow/humanresources/equalitydiversity/.

We endorse the principles of Athena Swan https://www.gla.ac.uk/myglasgow/humanresources/equalitydiversity/athenaswan/ and hold bronze, silver and gold awards across the University.

We are investing in our organisation, and we will invest in you too. Please visit our website https://www.gla.ac.uk/explore/jobs/ for more information.

Closing date 8 January 2026 at 23:45