Lead Data Scientist

The Lead Data Scientist leads the scientific strategy and technical execution for AI-powered recommendation systems, sitting at the intersection of machine learning, optimisation, generative AI, and decision intelligence. The role focuses on helping customers and partners discover the right products, optimise assortments, and make informed purchasing decisions through personalised, data-driven experiences at scale. The position involves leading cross-functional teams and working closely with data science, AIML engineering, product management, and user experience colleagues.

• Develop and deploy advanced recommendation models that deliver personalised product experiences aligned with customer needs and partner business objectives
• Design and implement optimisation frameworks that balance customer preferences, inventory availability, profitability, and operational constraints
• Lead the development of generative AI capabilities that enhance recommendation experiences through intelligent insights, explanations, and decision support
• Establish experimentation and measurement frameworks to evaluate recommendation quality, customer engagement, business impact, and AI effectiveness
• Partner closely with product managers, engineers, designers, and business stakeholders to define strategy, prioritise opportunities, and execute against the product roadmap
• Drive scientific rigour through model evaluation, offline benchmarking, online experimentation, and continuous improvement
• Mentor and develop data scientists and machine learning engineers, fostering technical excellence and a culture of learning
• Communicate complex analytical findings and strategic recommendations to technical and non-technical audiences, including senior leadership

• Master's degree in Computer Science, Operations Research, Statistics, Applied Mathematics, Engineering, or a related field; any suitable combination of education, experience, or training will be accepted
• 5+ years of experience leading the design, development, and deployment of data science, machine learning, optimisation, and operations research models at scale
• Strong proficiency in Python and experience leveraging machine learning frameworks such as PyTorch and TensorFlow
• Deep knowledge of recommendation systems, ranking models, personalisation, and predictive analytics
• Experience designing and evaluating large-scale experimentation platforms and A/B testing methodologies
• Experience building solutions using Agentic AI, Large Language Models (LLMs), and tool orchestration, and evaluating Agentic systems using automated and human-in-the-loop approaches
• Demonstrated expertise in modern recommender systems and emerging LLM/GenAI techniques, with the ability to lead the design of scalable personalisation solutions combining traditional recommendation methods, embeddings, semantic retrieval, ranking, and generative approaches
• Experience with cloud-native ML platforms such as Databricks and AWS SageMaker
• Demonstrated ability to lead complex cross-functional initiatives
• Strong communication skills

• Strong background in optimisation techniques, including linear programming, mixed-integer optimisation, reinforcement learning, and multi-objective optimisation