Data Science and AI Engineer, SEAA

About the role

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 con

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