Lead Data Scientist, Applied AI, SEAA
chanel asia pacific pte limited- Posted an hour ago
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Job Description
Why This Mission:
CHANEL SEAA is accelerating its data roadmap to expand from business intelligence towards decision intelligence: moving beyond reporting what happened to diagnosing issues, anticipating change and enabling confident, timely action for the brand, clients and business.
The Lead Data Scientist, Applied AI is SEAA's senior technical authority for data science, machine learning and applied AI. The role turns complex business opportunities into analytically rigorous, scalable and responsible intelligence products that strengthen client understanding, business performance, foresight, judgement and action. This is a player-coach role for someone who combines deep technical credibility with commercial judgement, remains hands-on in priority work, and can influence how leaders understand and act on evidence.
Working across Southeast Asia and Australia, the role partners with business leaders, Insight & Foresight, Data Platform and Engineering, Data Products and Stewardship, Data & AI Enablement, Technology, Global teams and specialist partners. The role adapts its approach to different market contexts and levels of data maturity, while developing solutions that can scale across the region.
Impact You Can Create In The Role:
Lead the Data Science and Applied AI Practice
Set technical direction, standards and leading practices across data science, machine learning, GenAI and agentic AI
Remain hands-on in priority work through problem framing, exploratory analysis, methodological design, prototyping, and code or model review
Guide practitioners and partners, uphold technical quality, transfer knowledge and help shape the future regional data science and applied AI capability
Turn Questions into Decision Intelligence
Partner with business leaders to identify the decisions that matter and define the expected client, boutique, brand, operational or financial value
Choose the simplest robust method, challenging whether AI is the right response
Connect internal client and business evidence with external market, cultural and industry signals in partnership with Insight & Foresight
Synthesise evidence into clear implications, uncertainties, trade-offs and recommended action
Build and Scale Advanced Analytics and Applied AI
Lead the design and validation of statistical, machine-learning, GenAI and agentic solutions for priority use case
Establish rigorous baselines, evaluation methods, assumptions and limitations
Work with engineering and Technology teams to productionise, monitor and improve solutions on governed, scalable foundations
Create reusable methods, assets and AI components that accelerate delivery and reduce duplication
Drive Adoption and Organisational Shift
Turn complex analysis into compelling, decision-focused stories for executive, market and functional audiences
Champion the shift from retrospective reporting towards diagnosis, foresight, judgement and proactive action
Embed intelligence into business rhythms and use adoption, feedback and outcomes to improve, scale, pause or retire solutions
Responsible AI and Continuous Improvement:
Ensure AI and data science solutions are developed and used responsibly, with appropriate attention to data privacy, explainability, model risk, security and governance
Stay close to emerging data science, GenAI and AI capabilities, assess relevance for Chanel use cases and continuously improve methods, components and delivery practices
The Profile We Are Looking For
Strong technical judgement, with the depth to challenge methods and identify weak assumptions
A hands-on mindset, with the willingness to work directly on priority use cases
Commercial and brand judgement, focused on decisions that matter for clients, boutiques and business performance
Clear and influential communication, turning complex evidence into a point of view and recommended action
A collaborative leadership style, raising the capability of colleagues and partners rather than becoming the sole expert
Your Success Measures
Business and Brand Value: Priority solutions improve decision quality, client experience, growth, operational effectiveness or productivity
Adoption and Decision Impact: Trusted intelligence is embedded in relevant business routines, products and workflow
Analytical Quality and Reliability: Solutions meet agreed standards for validity, performance, explainability, usability and documentation
Speed to Value and Reuse: Reusable methods, assets and components accelerate future delivery
Capability Maturity: Practitioners and partners demonstrate stronger technical practice and increasing independence
Responsible AI: Solutions operate with appropriate oversight and controls, with no critical privacy, security or misuse incidents
Organisational Shift: Leaders increasingly connect internal and external intelligence and move from reporting towards foresight, judgement and action
You are Energised by
Helping shape the future of an iconic House, using data and AI to strengthen CHANEL's singularity and enduring desirability
Deepening our understanding of clients, uncovering human truths and emerging possibilities that enable CHANEL to create meaningful, memorable experiences
Turning intelligence into imagination and action, helping the organisation see what others do not and make choices that set the standard for luxury
Pioneering responsible applications of AI, bringing together scientific rigour, creativity and human judgement in a way that feels distinctly CHANEL
Building a capability people are proud to create together, inspiring confidence, curiosity and new ways of thinking across the region
What You Will Bring: Capability Requirements
Data Science and Applied AI
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
Deep practical expertise in statistical modelling, machine learning, experimentation and model evaluation, with expert Python proficiency
Hands-on experience with enterprise GenAI, including RAG, embeddings, vector search, semantic search and agentic workflows
Recent direct contribution to data-science or applied-AI work, not only programme sponsorship, vendor oversight or people management
Technical Leadership and Production Fluency
Experience setting standards, reviewing technical work, mentoring practitioners and guiding partner delivery teams
Understanding of cloud AI and machine-learning platforms, model lifecycle and MLOps/LLMOps
Ability to partner with engineering teams on deployment, monitoring and lifecycle management without duplicating platform ownership
Business Problem Framing and Influence
Work with business and product partners to translate priority business questions into clear data-science and AI problems
Recommend the right analytical or AI approach, based on value, feasibility and technical rigour
Explain technical choices, assumptions, limitations and results clearly to non-technical stakeholders
Influence decisions through evidence and technical judgement, without taking ownership of business strategy or insight generation
Partner with Insight & Foresight on external signals, and with Data & AI Enablement on adoption and change
Responsible AI and Learning Agility
Strong understanding of privacy, security, explainability, bias, hallucination risk, human oversight and proportionate governance
Disciplined curiosity to assess fast-evolving capabilities and convert relevant advances into practical, governed solutions
At Chanel, we are focused on creating an inclusive culture that nurtures personal growth, contributing to collective progress. We believe the uniqueness of each individual increases the diversity, complementarity and effectiveness of our teams. We strongly encourage your application, as we value the perspective, experience and potential you could bring to Chanel.
More Info
Key Skills
GenAI
RAG embeddings
LLMOps
Applied AI
Model Evaluation
Machine-learning Platforms
Vector Search
Agentic Workflows
Model Lifecycle
Agentic AI
Machine Learning Experimentation

