On a typical day you will: 
 Lead, mentor and motivate a data science team (up to10 people) and set the technical direction and standards Collaborate within a cross-functional digital team, including Product Management, Engineering and Design. Bridge the gap between business problems and data science solutions, turning opportunities into impactful, production-ready models. Utilise strong analytical and modelling skills to design, build, and validate machine learning solutions, particularly tree-based methods (e.g. XGBoost, GBM, LightGBM), neural networks, and transformer-based approaches. Define and lead the end-to-end lifecycle of data products in an agile environment, from problem framing and experimentation through to deployment and monitoring. Conduct and document detailed analysis, including business case development, to prioritise use cases and clearly communicate trade-offs and expected value to stakeholders. Work closely with data and platform engineers on ETL pipelines, containerised solutions (e.g. Docker, Kubernetes), and cloud-based environments (e.g. GCP, AWS, Azure), with exposure to modern data platforms such as Snowflake and Databricks. Collaborate with cross-functional Agile teams (Engineering, Product, Design, QA) in remote and on-site settings, applying appropriate governance and engagement models to data projects. Provide BAU support for existing models and data products, while actively managing demand, prioritisation, and stakeholder expectations to create space for strategic, high-impact data science initiatives.  
