Head of Data Science
Role details
Job location
Tech stack
Job description
Set and deliver the bank's Data Science vision and roadmap, linking advanced analytics directly to commercial and risk outcomes. Build and lead a high-performing data science function with strong engineering discipline and clear standards. Act as a senior authority on AI and Generative AI, enabling responsible, secure and practical use across the bank. Champion transparency, explainability and ethical modelling in everything we deploy. Provide senior, hands-on leadership across statistical and machine-learning model development. Ensure strong feature engineering, validation, testing, explainability and reproducibility. Oversee scalable pipelines and reusable components, partnering with Engineering to embed models into production using modern CI/CD practices. Apply the Model Risk Management Framework proportionately and effectively. Represent Data Science at risk and governance forums, supporting robust challenge and approval. Ensure models meet PRA and FCA expectations for documentation, auditability and explainability. Oversee model monitoring, drift detection and remediation, with enhanced controls where risk is higher. Work closely with wider data teams to ensure models are built on high-quality, well-governed data. Promote reusable datasets and clear documentation to support scalable analytics. Advocate for improvements in data quality, definitions and controls.
Stakeholder engagement & people leadership
Build trusted relationships across Product, Risk, Compliance, Technology, Operations, Credit and Financial Crime. Lead, mentor and develop senior data scientists, supporting both technical depth and leadership growth. Foster an inclusive, collaborative and high-performance culture., Additional benefit allowance representing 7.5% of your annual salary allowing you the flexibility to decide your own benefits (or simply absorb this into your monthly income). 26 days' holiday increasing each year of service to 33 days Ability to buy and sell a further 5 days holiday each year 4 x Life Assurance Pension salary sacrifice Option for LinkedIn Learning license Family friendly policies Regular social activities and team events Charity Volunteering Day
Requirements
Experience applying data science in a financial services or highly regulated environment. Proven leadership of data science teams and ownership of data science strategy. Strong technical capability in Python, SQL and machine-learning development. Experience deploying batch and/or real-time models using modern engineering practices (CI/CD, versioning, automated testing). A solid understanding of UK model-risk regulatory expectations and proportionate governance. Confident communicator, able to explain complex ideas clearly to non-technical stakeholders. Experience in UK retail or SME banking. Familiarity with event-driven or real-time platforms (e.g. Kafka, Flink). Experience with tools such as Docker, Airflow, YARN, MLflow or BentoML. Background in credit, fraud, financial crime or operational analytics. Experience managing or overseeing third-party data or modelling vendors.