Lead Data Scientist

SR2 | Socially Responsible Recruitment | Certified B Corporation™; England; Hybrid; GBP 117,000 - 143,000; Full time

Job summary

A pioneering health technology company in London is seeking a mid-senior level Lead Data Scientist to innovate and lead model development in health AI. You will leverage machine learning to transform cardiovascular disease care by integrating multimodal biomedical data. Ideal candidates hold a PhD in relevant fields and have experience in developing deep learning models with tools like Python and TensorFlow. This is a unique opportunity to build a data science function from the ground up in a significant health-focused environment.

Qualifications

• 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.

Responsibilities

• Design, train, and deploy state-of-the-art machine learning and deep learning models.
• Apply advanced statistical and causal inference methods.
• Analyse and integrate multi-omics and clinical datasets.
• Build and productionise end-to-end ML pipelines.
• Collaborate with clinicians, engineers, and product teams.
• Contribute to model evaluation, explainability, and validation.

Skills

Machine Learning

Deep Learning

Statistical Modelling

Causal Reasoning

Python

PyTorch

TensorFlow

Data Analysis

Education

PhD in Machine Learning or related field

Tools

AWS

GCP

Azure

Job description

Principal AI Recruiter | Founder of The AI Collective

Lead Data Scientist | Life Science | Hybrid (London)

Up to £130k + equity

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.

Key Responsibilities

• 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.

About You

• 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.

Nice to Have

• 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.

Why Apply

• 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.

Seniority level: Mid‑Senior level

Employment type: Full‑time

Job function: Research and Information Technology

Industries: Research Services and Biotechnology Research