Location
London, with a fully hybrid working model and an expectation to be in the Holborn office at least once per month.

Hours
Full Time

Salary
Competitive salary

About the Role
Do you want to apply data science to one of the most important challenges in health research: enabling safe use of sensitive data at scale? In this role, you’ll develop privacy and security controls that protect participants while helping researchers generate insights faster and more safely. Our Future Health is an ambitious collaboration between the public, charity and private sectors, designed to help people live healthier lives for longer through better prevention, earlier detection and improved treatment of diseases. We will speed up the discovery of new methods of early disease detection, and the evaluation of new diagnostic tools, to help identify and treat diseases early, when outcomes are usually better. With over 2.5M volunteers across the UK, we’re now the world’s biggest health research programme of its kind, and our volunteer group is also more diverse than other, similar health research programmes. Technology and data are central to our mission. Our systems power websites, clinics across the UK, secure analytics and research systems, and pipelines that process highly sensitive health and genetic data. We are continuing to grow our data science capability to support this ambition.

To realise our ambition safely, we need to continue developing world-class approaches to data privacy, data security and responsible access to health data. We are looking for a Senior Data Scientist specialising in privacy, security and risk modelling, to help design, build and scale data-driven controls that protect participants while enabling cutting-edge research. This is a hands-on role where you’ll apply strong data science skills and an engineering mindset to real operational problems: safe outputs review, re-identification risk, privacy risks in trained models, synthetic data, and data-driven security controls.

You will work closely with teams across Product, Security, Engineering, Science, Data Protection, and Researcher Operations as well as external experts to develop practical, scalable and evidence-based solutions. You will be expected to work closely with users of these systems including researchers, Airlock reviewers, access governance reviewers and security specialists, to understand their workflows, pain points and risk decisions. A key part of the role will be turning complex privacy and security problems into trusted algorithms, evidence and automated support.

What you’ll do
- Develop data-driven approaches to disclosure control and safe outputs review, supporting the scaling of our TRE (Trusted Research Environment) Airlock. This may include building algorithms and automated review tools to classify outputs, detect potentially disclosive content, identify patterns of risk, and provide explainable decision support for human reviewers.
- Work directly with Airlock reviewers and operational users to understand where automation can help, where human judgement is essential, and how tools should be designed to support consistent, auditable and proportionate decisions.
- Contribute to our approach to de-identification and re-identification risk assessment, helping us assess how privacy risk changes across datasets, access models and analytical outputs.
- Help us develop our approaches to safe AI using health data, including how we assess and manage privacy risks associated with trained models.
- Explore and develop approaches to synthetic data generation, assessing how synthetic data can be used safely and usefully, and how to evaluate the privacy, fidelity and utility trade-offs of different approaches.
- Collaborate with our Information Security team on data-driven security control assessment, threat detection and monitoring approaches, including identifying signals of risky behaviour, anomalous activity or misuse of data access environments, and cyber risk quantification.
- Build prototypes and production-quality code, working with engineers to turn promising approaches into robust, maintainable and auditable tools.
- Work closely with governance, legal, ethics and operational colleagues to ensure technical controls fulfil their requirements.
- Keep up with emerging methods in statistical disclosure control, privacy-enhancing technologies, AI security, synthetic data, de-identification and privacy-preserving computation.

Experience
- Significant experience applying data science, machine learning, statistical modelling or advanced analytics to complex real-world datasets.
- Strong applied statistical expertise, including the ability to quantify risk and uncertainty, evaluate assumptions, design validation approaches, interpret imperfect or incomplete evidence, and communicate the limitations of statistical or machine learning models.
- Strong Python skills and experience writing maintainable, production-quality code.
- Experience working in cross-functional teams with software engineers, data engineers or platform teams to design and deliver data products, pipelines, analytical services or decision-support tools.
- Applied machine learning experience and understanding of common privacy attacks against data and models, such as memorisation, membership inference, attribute inference, model inversion or leakage through model outputs.
- Exposure to privacy-preserving machine learning, privacy-enhancing technologies or adjacent research areas (e.g. federated learning, secure aggregation, differential privacy, or confidential computing).
- Experience working with sensitive, confidential or regulated data and a strong understanding of privacy, confidentiality or information security risks.
- Ability to translate ambiguous operational, governance or security problems into clear data science questions and practical technical requirements.
- Good communication skills, with the ability to explain complex technical concepts to non-specialist stakeholders.
- A pragmatic, delivery-focused mindset.

About you
We welcome applications from all who may not feel they match the full criteria, so if you have most of the above, we'd like to hear from you.

It would be a bonus if you have any of the following experience:
- Working with health data or biomedical research data, electronic health records, and ideally genomic data.
- Developing models, algorithms or rule-based systems that support human decision-making, ideally where explainability, auditability and risk management are important.
- Anomaly detection, behavioural analytics, security monitoring or detection engineering.
- Risk quantification methods from fields such as actuarial science, epidemiology, operational research, or cyber risk.
- Familiarity with UK data protection, research governance or health data access expectations.
- Experience with PETs or privacy-preserving ML frameworks such as Flower, Opacus, TensorFlow Federated or similar.
- Cloud platforms, containerisation, CI/CD, MLOps or production ML systems.
- Experience evaluating synthetic data using both utility and privacy metrics.

Hiring Process
We feel hiring should be transparent and give you a real sense for what Our Future Health is like. Here's what you can expect:
- Initial chat with our Talent team (30 min) to get to know each other, discuss the role, and answer any questions you have.
- 1st interview with our Head of Data Operations (25 min) - an opportunity to meet your potential line manager and align on role expectations.
- Technical interview with 2-3 members from our tech and data teams (60 min), including a short task to prepare