Lead Data Scientist in England

We’re partnering with a pioneering health technology company using machine learning and predictive analytics to transform how cardiovascular and metabolic diseases are detected, treated, and ultimately prevented. Their mission is to use AI and data science to extend global health span by identifying individuals at risk of disease before symptoms occur.

You’ll join as the first Data Science hire, building the foundation of a platform that integrates multi-modal biomedical data, deep learning models, and large-scale population datasets such as UK Biobank and Our Future Health. This is a rare opportunity to lead model development in a setting that bridges scientific rigour with production-grade engineering.

• Design, train, and deploy state-of-the-art machine learning and deep learning models to predict health outcomes and disease progression.
• Apply advanced statistical and causal inference methods (e.g. survival analysis, time-to-event modelling, propensity scoring, Mendelian randomisation).
• Analyse and integrate multi-omics and clinical datasets to uncover novel biomarkers and risk factors.
• Build and productionise end-to-end ML pipelines, from research to deployment.
• Collaborate with clinicians, engineers, and product teams to translate scientific findings into scalable tools.
• Contribute to model evaluation, explainability, and validation across diverse data sources.

• PhD in Machine Learning, Computational Biology, Statistics, Bioinformatics, or a related quantitative field.
• Background in cardiovascular, cardiometabolic, or precision medicine research.
• Proven experience developing deep learning models using Python, PyTorch, or TensorFlow.
• Strong understanding of statistical modelling, causal reasoning, and predictive analytics.
• Demonstrated experience working with large-scale health, genomic, or biobank datasets (e.g. UK Biobank, All of Us, Our Future Health).
• Exposure to production deployment and model lifecycle management (MLOps awareness a plus).
• Strong communicator with the ability to operate between science and engineering teams.

• Experience integrating multi-omic or imaging data with clinical outcomes.
• Knowledge of cloud platforms (AWS, GCP, or Azure) and distributed computing tools (PySpark, Dask, or Ray).
• Familiarity with reinforcement learning or causal ML for adaptive interventions.

• Join a company combining scientific excellence, AI innovation, and real-world health impact.
• Work with world-leading clinicians and researchers.
• Shape a greenfield data science function from day one.

If you’re passionate about applying advanced machine learning to improve cardiovascular and metabolic health at population scale, we’d love to hear from you.