Senior Mathematical Modeller - Disease Modelling

HealthLumen are specialists in applying precision epidemiological modelling to patient populations, providing exceptional insights into the current prevalence and the future health and economic burden of disease. 

Our powerful genetic database analysis and microsimulation modelling technologies enable us to deliver unprecedented accuracy for modelling the current and future burden of chronic and rare genetic diseases.  

We are now expanding our team to include a Senior Mathematical Modeller. The role will involve developing mathematical models, epidemiological analyses and economic evaluations of the dynamics of non-communicable diseases (NCDs) and genetic diseases, and models to evaluate the impact of interventions over the both the short and long term, including data analyses to parameterise the models. 

Primary responsibilities 

• Mathematical modelling: Develop microsimulation models to simulate the dynamics of chronic and genetic diseases, incorporating relevant epidemiological, clinical, and demographic data. Implement and validate models using statistical and computational methods.
• Data analysis: Collect, preprocess, and analyse data ensuring the accuracy and reliability of input data for models. Collaborate with data scientists and other stakeholders to integrate diverse datasets.
• Model calibration and optimisation: Calibrate models based on observed data and refine parameters to improve model accuracy and predictive capabilities. Implement optimisation techniques to enhance model performance and robustness.
• Scenario analysis: Conduct scenario analyses to assess the impact of interventions, treatments, and public health policies on the prevalence and progression of diseases. Communicate findings to interdisciplinary teams and contribute to evidence-based decision-making.
• Research and development: Stay abreast of the latest research in mathematical modelling, epidemiology, and disease management to inform model development and refinement.
• Documentation and reporting: Prepare clear and concise documentation of modelling methodologies, assumptions, and results. Present findings through reports, presentations, and scientific publications.
• Support development of HealthLumen’s strategy and product road map. 

Key skills 

• Proven experience in mathematical modelling, particularly in the context of NCD's
• Highly numerate, understanding of stochastic calculus
• Proficient in programming languages, including R (expert) 
• Strong team working abilities, with ability to communicate with software developers, epidemiologists and collaborators
• Excellent problem solving
• Experience of project and/or team management
• Experience of working across public and private sectors
• Research skills 

Qualifications

• MSc/PhD in Applied Mathematics, Epidemiology, Statistics, or a related field 

Personal qualities 

• A commitment to improving health outcomes
• Excellent analytical and problem-solving skills
• Ability to work independently and collaborate effectively in interdisciplinary teams
• Calm and focused under pressure
• Strong communication skills, with the ability to convey complex concepts to diverse audiences.
• Willingness to acquire new knowledge and skills
• Comfortable adapting to sometimes rapidly changing circumstances 

Location: This role operates primarily on a remote working arrangement that will require occasional travel to our Central London office (est. twice monthly). Candidates need to be based within reasonable commuting distance of London.  

Apply Today 

If you have the relevant skills and experience, click the apply button above and email your CV and covering letter as one document. Your covering Letter should detail how you meet the requirements for the role.

Applications are reviewed daily, so we encourage you to apply today. Advertising will remain live until the recruitment process is complete.

If you have any questions about this role, don’t hesitate to get in touch - e:recruitment@healthlumen.com 

Closing date for applications: 22.08.25 (if not before)