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Careers

Where do our graduates go?

Graduates of the Applied Statistical Modelling and Health Informatics MSc go on to work at the forefront of evidence-based health innovation, from predicting disease risk and improving clinical trials to developing digital health tools and AI-driven solutions. Career destinations include:

  • NHS and Public Health organisations, including roles in healthcare analytics, service evaluation, digital health, policy, and epidemiology.
  • Pharmaceutical, biotech, and health-tech sectors, including roles in clinical trials, real-world evidence, medical statistics, and clinical informatics.
  • AI, machine learning, and data science roles focused on predictive modelling, digital biomarkers, mental health, and precision medicine.
  • Academic institutions and research centres, including clinical trials units, genomics facilities, and interdisciplinary health data science groups.
  • Consultancy, policy, and industry roles across life sciences, finance, insurance, marketing, and government.
  • Non-profits and international organisations, particularly in global health data, environmental health, and digital innovation.

Many graduates choose to pursue further study and are well prepared for PhD and research careers in areas such as biostatistics, health data science, bioinformatics, precision psychiatry, neurodegeneration, epidemiology, and AI for healthcare. The department hosts a fully funded doctoral training programme through the EPSRC Centre for Doctoral Training in Data-Driven Health (DRIVE-Health).

Whether your ambitions lie in clinical trials, health-tech innovation, academic research, or data-intensive roles beyond healthcare, the MSc in Applied Statistical Modelling and Health Informatics provides the technical foundation, practical experience, and professional skills needed to support your next steps.

Graduate profiles

Chris (PGCert, 2021–2022)
Chris is a Translational Health Data Scientist and Programme Manager in Psychedelic-Assisted Cancer Therapy. He applies statistical modelling and health informatics to mental-health and clinical data, integrating computational and clinical perspectives to advance evidence-based, data-driven healthcare research.

Ihsan (PGCert, 2020–2021)
Ihsan is an Applied Statistician at the Oxford University Clinical Research Unit (OUCRU), specialising in infectious disease and health inequity research. His statistical work has been published in PLOS Medicine and BMJ Global Health, contributing to international clinical research and informing public health policy.

Diya (MSc, 2022–2023)
Diya is a Medical Statistician at Oxford’s NPEU Clinical Trials Unit, specialising in real-world data and survival analysis. Her MSc project explored links between air pollution exposure and mental-health service use among young people with anxiety disorders, generating insights relevant to population health and environmental policy.

Misha (PGDip, 2023–2024)
Misha is a PhD Researcher at City St George’s University, funded by the Alzheimer’s Society, where she applies artificial intelligence to retinal imaging to predict dementia risk. Her work combines large-scale data modelling with neuroscience to support earlier detection of neurodegenerative disease.

Harris (MSc, 2021–2022)
Harris is a Health Data Analyst at Prostate Cancer UK, using routinely collected clinical data to strengthen evidence-based care. For his MSc dissertation, he developed a machine-learning model using Fitbit sleep data to predict depression risk, demonstrating the application of digital health data to real-world clinical questions.

Lauren (MSc, 2023–2024)
Lauren is a Higher Statistical Officer at the Department for Work and Pensions (DWP) and has previously interned at the health technology company thymia. Her MSc dissertation explored the role of cognition in the relationship between depression and speech, combining applied statistics with real-world mental health research.