Senior Data Scientist (Privacy & Security Controls)

• 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

Benefits

• 30 days annual leave plus bank holidays.
• Generous company pension package with employer contributions of up to 12%
• We have a lovely new office in Holborn, Central London and we offer flexible and remote working arrangements.
• We offer all employees a life assurance policy.
• Training support to help you achieve your potential.
• All employees are covered by a group income protection scheme to support them in the event of long term sickness.

Preferred experience

• 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)
• A pragmatic, delivery-focused mindset
• 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
• 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
• 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
• 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
• Experience working with sensitive, confidential or regulated data and a strong understanding of privacy, confidentiality or information security risks
• Significant experience applying data science, machine learning, statistical modelling or advanced analytics to complex real-world datasets
• Strong Python skills and experience writing maintainable, production-quality code
• 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

Recruitment 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) - this is an opportunity to meet your potential line manager and for you both to align on role expectations
• Technical interview with 2-3 members from our tech and data teams (60 min). This will include a short task you’ll need to prepare in advance of the interview and is designed to get a sense for how you’d approach the real-world responsibilities of this role
• Final stage competency interview (60 min) with a small cross-functional panel of your potential new stakeholders. This will focus on how you collaborate, work with stakeholders, and navigate ambiguity and challenges in real-world projects