Job Description

Position Summary

We are seeking a Lead Data Scientist AI/ML to provide technical leadership across artificial intelligence and machine learning initiatives and deliver reliable, scalable, and impactful data-driven solutions. This role is responsible for defining AI/ML strategy, leading complex modelling projects, developing team capability, and partnering with product, technology, commercial, and business stakeholders. The ideal candidate combines strong expertise in machine learning, statistics, software engineering, and data platforms with proven leadership skills and the ability to translate advanced AI/ML capabilities into practical business outcomes.

Key Responsibilities

• Lead, mentor, and develop data scientists, machine learning engineers, and related analytical specialists, promoting a collaborative, inclusive, and high-performing culture
• Define and implement artificial intelligence and machine learning strategies, standards, methodologies, and best practices aligned with organisational objectives
• Lead the delivery of AI/ML projects across the full lifecycle, including problem definition, data preparation, feature engineering, model development, validation, deployment, monitoring, and continuous improvement
• Partner with Product Management, Engineering, Data Engineering, Analytics, Operations, and business stakeholders to identify opportunities and define measurable outcomes for AI and machine learning solutions
• Provide technical leadership on supervised and unsupervised learning, deep learning, generative AI, natural language processing, computer vision, predictive modelling, experimentation, and optimisation initiatives
• Evaluate emerging artificial intelligence and machine learning technologies, tools, frameworks, and foundation models to identify opportunities for innovation and competitive advantage
• Translate complex AI/ML concepts, model behaviour, and analytical findings into clear recommendations for technical and non-technical stakeholders
• Establish and maintain standards for data quality, model development, validation, reproducibility, documentation, explainability, and responsible use of artificial intelligence
• Review analytical approaches, model architectures, code, assumptions, performance metrics, and results to ensure accuracy, robustness, scalability, and business relevance
• Oversee the development, deployment, and ongoing performance monitoring of machine learning models, AI services, and data products
• Identify and manage risks relating to bias, fairness, privacy, security, explainability, data protection, model drift, hallucination, and regulatory compliance
• Promote the use of MLOps and software engineering practices, including version control, automated testing, continuous integration, model registries, experiment tracking, and infrastructure automation
• Support recruitment, onboarding, performance management, career development, and succession planning for data science and machine learning team members
• Manage priorities, delivery plans, dependencies, resources, and risks across multiple AI/ML initiatives
• Identify opportunities to improve data availability, modelling techniques, AI/ML tooling, automation, model operations, and team productivity
• Provide regular reporting to senior leadership on project progress, business impact, model performance, team capacity, technical risks, and future AI/ML requirements

Required Qualifications

• Significant experience in data science, artificial intelligence, machine learning, statistics, quantitative analysis, or a related technical discipline, including experience leading AI/ML projects or teams
• Proven experience applying machine learning and statistical techniques to solve complex business, customer, or operational problems
• Strong understanding of supervised and unsupervised learning, deep learning, predictive modelling, feature engineering, model evaluation, experimentation, and statistical inference
• Practical experience with Python and common data science, machine learning, and deep learning libraries and frameworks
• Experience working with SQL, relational or non-relational databases, data warehouses, data lakes, and large or complex datasets
• Experience taking AI/ML models or analytical solutions from development through deployment, monitoring, governance, and ongoing improvement
• Experience managing or mentoring data scientists or machine learning engineers through coaching, technical review, professional development, and team planning
• Ability to communicate complex artificial intelligence and machine learning concepts, model limitations, analytical findings, and business recommendations clearly to both technical and non-technical stakeholders
• Strong problem-solving, critical thinking, decision-making, organisational, and stakeholder management skills
• Strong understanding of data governance, information security, data protection, model risk, AI ethics, and responsible artificial intelligence principles
• High attention to detail balanced with the ability to take a strategic, organisation-wide view of AI/ML and analytical delivery

Preferred Qualifications

• Experience leading distributed or cross-functional data science, artificial intelligence, and machine learning teams
• Experience with generative AI, large language models, prompt engineering, retrieval-augmented generation, natural language processing, computer vision, time series forecasting, recommendation systems, or optimisation
• Experience with cloud data and machine learning platforms, including AWS, Microsoft Azure, or Google Cloud
• Experience with MLOps, model governance, feature stores, experiment tracking, model monitoring, model registries, and automated machine learning workflows
• Experience working with distributed data processing technologies, data pipelines, APIs, software engineering practices, and modern data platforms
• Experience with containerisation, orchestration, infrastructure as code, CI/CD, and production-grade machine learning services
• Relevant degree or professional qualification in artificial intelligence, machine learning, data science, computer science, statistics, mathematics, engineering, or a related field
• Research experience, published work, patents, or contributions to open-source artificial intelligence and machine learning projects
• Experience working in an environment with formal information security, data protection, quality, responsible AI, or regulatory requirements