Lead Data Scientist, Applied AI

About the role

Chanel SEAA is looking for a Lead Data Scientist, Applied AI, acting as the senior technical authority for data science, machine learning and applied AI across Southeast Asia and Australia. The role turns complex business opportunities into rigorous, scalable and responsible intelligence products, working with business leaders, Insight & Foresight, Data Platform, Engineering, Data Products and Stewardship, Data & AI Enablement, Technology and global teams. It is a player-coach position combining deep technical credibility with commercial judgement.

Key responsibilities

• Lead the Data Science and Applied AI practice, setting technical direction, standards and leading practices across data science, machine learning, GenAI and agentic AI.
• Remain hands-on in priority work: problem framing, exploratory analysis, methodological design, prototyping and code or model review.
• Guide practitioners and partners, uphold technical quality, transfer knowledge and shape the regional data science and applied AI capability.
• Partner with business leaders to turn questions into decision intelligence, choosing the simplest robust method and synthesising evidence into clear implications and recommended actions.
• Design and validate statistical, machine-learning, GenAI and agentic solutions, establish baselines and evaluation methods, and work with engineering teams to productionise, monitor and improve them.
• Create reusable methods, assets and AI components, and turn complex analysis into decision-focused stories for executives and other audiences.
• Champion the shift from retrospective reporting to diagnosis, foresight and proactive action, and ensure responsible AI with attention to privacy, explainability, model risk, security and governance.

Required profile

• At least 7 years of experience in data science and machine learning, including recent hands-on delivery of complex analytical solutions.
• Practical experience applying GenAI or agentic AI in enterprise settings is strongly preferred.
• Recent direct contribution to data-science or applied-AI work, not only programme sponsorship, vendor oversight or people management.
• Strong technical judgement, a hands-on mindset, commercial and brand judgement, and clear, influential communication.
• A collaborative leadership style that raises the capability of colleagues and partners.

Required skills

• Expert Python proficiency and deep expertise in statistical modelling, machine learning, experimentation and model evaluation.
• Enterprise GenAI experience, including RAG, embeddings, vector search, semantic search and agentic workflows.
• Understanding of cloud AI and ML platforms, model lifecycle and MLOps/LLMOps.
• Ability to review technical work, mentor practitioners and guide partner delivery teams.
• Ability to partner with engineering teams on deployment and monitoring without duplicating platform ownership.