Lead Data Scientist, Applied AI, SEAA,

Job description

At Chanel SEAA, we aim to leverage data to power sustainable growth, improve business performance and elevate luxury client experiences across the region. The Data Science and AI Engineer, designs and delivers AI products that transform data into business value. Working across CRM, retail, merchandising, marketing, operations and beyond, the role builds advanced analytics, machine learning, generative AI and agentic solutions to improve decision-making, elevate client engagement, automate workflows and drive innovation, while ensuring responsible use in line with Chanel’s standards of excellence and discretion. Impact You Can Create In The Role : Develop Advanced Analytics & Machine Learning Solutions Design and build machine learning, statistical models to address business questions across client intelligence, CRM, retail performance, merchandising, marketing and operations. Apply techniques such propensity modelling, recommendation algorithms, forecasting, classification, clustering, uplift modelling and anomaly detection to generate scalable analytical and ML solutions. Build Robust Data Science Pipelines Prepare, transform and engineer large-scale structured and unstructured datasets for modelling, experimentation and analytical product development. Develop reusable data science workflows, feature engineering logic, model training pipelines and evaluation frameworks to improve scalability and repeatability. Work with data engineering and technology teams to ensure data pipelines, model inputs and analytical datasets are reliable, well-structured and fit for production use. Prototype & Develop Agentic AI Solution: Develop and evaluate Generative AI solutions, including prompt-based workflows, retrieval-augmented generation, embedding pipelines, and other LLM-enabled applications. Use Python, LLM APIs, LangChain / LangGraph, embeddings, vector search, RAG and tool calling to build reusable, scalable and governed AI components. Experimentation & Value Measurement: Define test-and-learn approaches, pilots, control groups and impact measurement frameworks to validate recommendations, quantify business outcomes and support adoption decisions. Deploy, Monitor and Optimize Models Support deployment of machine learning and AI solutions into production or business-facing tools in partnership with data engineering and technology teams. Monitor model performance, accuracy, stability, drift and business impact, identifying opportunities for retraining, optimization or enhancement. Document model logic, assumptions, limitations, performance metrics and technical methodologies to ensure transparency, maintainability and knowledge transfer. Responsible AI & 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. Your Success Measures • Business Value & Activation: Data science and AI solutions create measurable business value, improve decision-making and support growth, client experience or operational effectiveness. • Model Performance & Reliability: Models and analytical solutions meet agreed performance, stability, explainability and usability expectations, and remain reliable after deployment. • Use Case Conversion: High-value business opportunities are translated into clear, prioritized and deliverable data science / AI use cases with practical delivery plans. • Adoption & Usage: Analytical and AI outputs are embedded into dashboards, data products, workflows or decision routines and are actively used by target business users. • Speed-to-Value: Reusable modelling assets, frameworks and AI components reduce duplicated effort and accelerate delivery of future analytics and AI use cases. • Stakeholder Satisfaction: Positive feedback from business and technical stakeholders on the relevance, usability, transparency and impact of data science and AI solutions. • Responsible AI & Compliance: No critical incidents related to model misuse, data privacy, security, governance or irresponsible AI practices. You are Energized by : Solving Business Problems with Data: Turning business questions into structured analytical problems, meaningful insights and practical recommendations that improve decision-making. Building Data Science and AI Solutions: Developing models, analytical assets and AI-enabled solutions that can create measurable business value and be reused across different use cases. Exploring GenAI and Agentic AI: Experimenting with emerging AI capabilities, including LLMs, agentic workflows and automation, and translating them into practical, responsible business applications. Learning and Adapting Quickly: Staying close to the rapid evolution of AI, tools and methods, with the curiosity and agility to continuously learn, test and improve. Telling Stories with Data: Translating complex analytical outputs into clear visual stories, business implications and recommendations that stakeholders can understand, trust and act on. Collaborating Across Teams: Working closely with business, data engineering, technology, BI, analytics and global teams to turn ideas into scalable solutions and accelerate intelligence towards the LEAP ambition. What You Will Bring- Capability Requirements : ML, AI and GenAI Expertise Strong practical experience applying machine learning techniques to solve business problems. Expert-level proficiency in Python and its data science ecosystem, hands-on experience with deep learning frameworks such as PyTorch or TensorFlow. Familiarity with Docker, Kubernetes, Git-based workflows, model deployment, monitoring, retraining, and lifecycle management. Experience with GenAI techniques such as prompt engineering, RAG pipelines, embeddings, vector databases, semantic search or text analytics is a plus. Data Platform Proficiency Strong experience with cloud-based AI and machine learning platforms such as databricks, AWS SageMaker, Azure Machine Learning. Ability to lead scalable model development, deployment, and monitoring. Exposure to GenAI tools and platforms such as Azure AI Foundry, LangChain / LangGraph, or similar enterprise AI platforms is preferred. Experimentation & Measurement Comfortable designing pilots, A/B tests, control groups and impact measurement frameworks to validate analytical recommendations and quantify business outcomes. Responsible AI Good understanding of Responsible AI principles, including data privacy, security, explainability, bias, hallucination risk, human oversight and appropriate governance controls for ML, GenAI and agentic AI solutions. AI-Native Engineering & Continuous Evolution Experience in utilizing AI tools, such as Cursor, GitHub Copilot, or Claude Code as a core part of development workflow Strong learning agility to keep pace with the rapid evolution of AI, GenAI and agentic AI, quickly assess emerging capabilities and adapt relevant methods into practical business solutions. Business Stakeholder Communication Experience engaging with business stakeholders is a plus, with the ability to translate complex analytical concepts into clear, accessible business language . 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 .