Senior Data Scientist (Privacy & Security Controls)

Join our team as a Senior Data Scientist focused on Privacy & Security Controls. In this role, you will develop data-driven approaches to disclosure control and safe outputs review, contribute to de-identification and re-identification risk assessment, and help develop approaches to safe AI using health data. You will collaborate with various teams, build prototypes and production-quality code, and stay updated on emerging methods in statistical disclosure control and privacy-enhancing technologies.

• Develop data-driven approaches to disclosure control and safe outputs review, supporting the scaling of our Trusted Research Environment (TRE) Airlock.
• Collaborate 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.
• 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
• A user-focused approach to technical delivery: you are comfortable working with people who operate, review, govern or depend on data systems, and can translate their needs into technical requirements
• Strong Python skills and experience writing maintainable, production-quality code
• Significant experience applying data science, machine learning, statistical modelling or advanced analytics to complex real-world datasets
• 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
• Good communication skills, with the ability to explain complex technical concepts to non-specialist stakeholders
• A pragmatic, delivery-focused mindset
• Exposure to privacy-preserving machine learning, privacy-enhancing technologies or adjacent research areas (e.g. federated learning, secure aggregation, differential privacy, or confidential computing)
• 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
• Ability to translate ambiguous operational, governance or security problems into clear data science questions and practical technical requirements
• Experience working with sensitive, confidential or regulated data and a strong understanding of privacy, confidentiality or information security risks
• 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
• Experience with PETs or privacy-preserving ML frameworks such as Flower, Opacus, TensorFlow Federated or similar
• Familiarity with UK data protection, research governance or health data access expectations
• Cloud platforms, containerisation, CI/CD, MLOps or production ML systems
• Experience evaluating synthetic data using both utility and privacy metrics