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About the Role

Develop privacy and security controls that protect participants while helping researchers generate insights faster and more safely. Apply data science skills and an engineering mindset to real operational problems like safe outputs review, re-identification risk, privacy risks in trained models, synthetic data, and data-driven security controls.

Responsibilities

• Develop data-driven approaches to disclosure control and safe outputs review, supporting the scaling of our TRE (Trusted Research Environment) Airlock.
• Build 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.
• 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.

Requirements

• 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.
• A user-focused approach to technical delivery: comfortable working with people who operate, review, govern or depend on data systems, and can translate their needs into technical requirements.
• 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.

Skills

• Data Science
• Machine Learning
• Statistical Modelling
• Advanced Analytics
• Python
• Privacy-Preserving Machine Learning
• Privacy-Enhancing Technologies
• Federated Learning
• Secure Aggregation
• Differential Privacy
• Confidential Computing
• Information Security
• Disclosure Control
• AI Security
• Synthetic Data Generation
• De-identification
• Privacy-Preserving Computation

Location

• Holborn, London

Work Type

• Hybrid
• Full-time

Experience Level

• Senior

Salary/Compensations

• £80,000

Benefits

• Generous Pension Scheme – employer contributions of up to 12%
• 30 Days Holiday pro rata + Bank Holidays
• Enhanced Parental Leave
• Cycle to Work Scheme
• Home & Tech Savings
• EV Scheme
• £1,000 Employee Referral Bonus
• Wellbeing Support – Access to Mental Health First Aiders, plus 24/7 online GP services and an Employee Assistance Programme
• Flexible and remote working arrangements

About the Company

• 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 web sites, clinics across the UK, secure analytics and research systems, pipelines that process highly sensitive health and genetic data, and 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.

Equal Opportunity

• At Our Future Health, we recognise the importance of having a diverse workforce and ensuring that all candidates, regardless of their background, have equitable access to our application process.
• We proactively encourage applicants who identify as having a disability, neurodiversity, or long-term health conditions to let us know if they require any reasonable adjustments as part of their application process.
• If you do require any reasonable adjustments, please email us at talent@ourfuturehealth.org.uk