Data Scientist, VP

Key Responsibilities

• Lead, drive, and embed the design and implementation of data science tools and methods across the organisation
• Use data translation skills to work closely with business stakeholders to define clear, detailed business questions and opportunities suitable for advanced analytics
• Ensure data science solutions are developed with a Group-wide mindset and aligned to strategic objectives
• Build strong relationships with stakeholders to understand complex business requirements and identify appropriate analytical approaches
• Form hypotheses and identify relevant data sources to develop effective analytics and machine learning solutions
• Communicate advanced analytics insights and opportunities through clear data visualisation, highlighting benefits, trade-offs, and ethical considerations
• Lead direct reports and contribute across wider multi-disciplinary teams using agile and scrum methodologies
• Select, build, train, test, and validate complex machine learning models, considering model risk, valuation, governance, scalability, and ethics
• Ensure models are unbiased, compliant, and ready for implementation and production use
• Lead stakeholder engagement activities to ensure project outcomes meet business needs and expectations
• Identify opportunities to enhance external customer propositions using data science capabilities aligned to Group strategy
• Explore and analyse data responsibly, ensuring appropriate use of golden source data
• Establish and promote best practices for machine learning development, data access, and data procurement

Required Skills and Experience

• Strong expertise in data science, statistics, and applied mathematics
• Proven experience designing and delivering machine learning models end-to-end
• Experience applying software engineering and product development principles within data science
• Strong understanding of model governance, ethics, bias mitigation, and risk management
• Experience leading teams and delivering outcomes in agile, multi-disciplinary environments
• Strong stakeholder management and communication skills, with the ability to translate complex analytics into business-friendly insights
• Experience working with large, complex datasets and enterprise data platforms