Principal Epidemiologist Job at Canary Wharf, London, Hybrid Full Time NEW

The MHRA is transforming. Through enabling innovation, making the right judgements of the benefits and risks of medical products and forging the right partnerships in the UK and internationally, it aims to deliver world class regulation and improved outcomes for UK patients.

We are currently looking for experienced Principal Epidemiologist to join our Scientific Data & Insight Function in the Safety and Surveillance group.

Within a week of working here, your day could look like this: Apply your expertise and networking skills to drive forward an agenda of modernising our vigilance systems. This will include establishing new methodological approaches, forging collaborations with academia, and enabling us to deploy new state of the art methods to fundamentally change our approach to vigilance.

Operating with significant scientific autonomy, the Principal Epidemiologist will act as a thought leader and methodological innovator – driving forward high-profile projects, advising senior leadership, and contributing to the MHRA’s standing as a global leader in regulatory epidemiology. The role will require building and maintaining strategic partnerships with academic institutions, healthcare organisations, regulators and international bodies, ensuring the Agency’s work is informed by and contributes to the wider scientific and regulatory network. 

The successful candidate will have a doctoral qualification (or equivalent advanced experience) in epidemiology, statistics, or a closely related quantitative discipline. 

You will have extensive expertise in the design, implementation, and critical appraisal of advanced epidemiological and statistical methods, including the ability to innovate, adapt, or extend methodologies to address complex, high-uncertainty regulatory challenges.

Advanced proficiency in programming and computational epidemiology, with expert-level skills in tools such as Python, R, or Stata for handling large, complex, and linked healthcare datasets. Demonstrated commitment to reproducibility, scalability, and transparency in data science.

• Demonstrated leadership in solving complex, strategic challenges, using advanced epidemiological methods.
• A strong track in high quality research as evidenced by a substantial publication record. 
• Internationally recognised expertise in at least one of the following domains, with significant influence on practice, policy, or methodological development: 

• • Bayesian methods
  • Causal inference
  • Dynamic benefit-risk assessment
  • Genetic and molecular epidemiology
  • Machine learning and AI
  • Simulation-based benefit-risk models
  • Systems epidemiology
  • Similar advanced and novel methods

Closing date: 01 June 2026