Data Science and AI Specialist

Data Science and AI Specialist – University of Glasgow

Posted 20 June 2025 | Salary Grade 7: £41,064 – £46,049 per annum | End date 08 January 2026 |

Location:

Glasgow | Full‑time (35 hours per week) | Funding for up to 3 years initially.

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.

• 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.
• Develop reproducible and transparent analytical pipelines, ensuring data provenance, version control and adherence to ethical and governance standards.
• Work 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.
• Lead on technical aspects of data integration, statistical and machine‑learning model development, validation, interpretability and deployment within the secure TRE environment.
• Ensure all research activities comply with NHS data governance, ISO standards and the TRE’s ethical frameworks.
• Contribute to demonstration and exemplar projects (e.g., multimodal data integration, digital phenotyping, predictive analytics) that highlight the TRE’s analytical and AI capabilities.
• 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.
• Support the training and mentoring of researchers and students in applied health data science, statistical methods and TRE workflows.
• Perform administrative and governance‑related tasks relevant to TRE operations, including documentation, data access tracking and project coordination.
• Keep up to date with current knowledge and recent advances in the field/discipline.
• Contribute to research outputs, grant applications and dissemination activities that strengthen TRE capabilities and support collaborative funding bids.
• Participate and engage with national and cross‑institutional AI/TRE initiatives and networks as appropriate.
• Undertake any other reasonable duties as required by the Head of School / Director of Clinical TRE.
• Contribute to the enhancement of the University’s international profile in line with the University Strategy.

Essential

• 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.
• 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., Tensor Flow, PyTorch,…