Data Science and AI Engineer, SEAA

About the Role

CHANEL SEAA is looking for a Data Science and AI Engineer to turn data into measurable business value across the region. The role focuses on creating AI-driven products and analytical solutions that support sustainable growth, improve business performance, and enhance luxury client experiences.

You will work across CRM, retail, merchandising, marketing, operations, and related functions to build advanced analytics, machine learning, generative AI, and agentic solutions. The position also calls for responsible AI practices aligned with CHANEL’s expectations for quality, discretion, privacy, and governance.

What You Will Do

• Design and develop machine learning and statistical models to answer business questions across client intelligence, CRM, retail performance, merchandising, marketing, and operations.
• Use approaches such as propensity modelling, recommendation systems, forecasting, classification, clustering, uplift modelling, and anomaly detection to create scalable analytical solutions.
• Prepare, clean, transform, and engineer large structured and unstructured datasets for modelling, experimentation, and analytical product development.
• Build repeatable data science workflows, feature engineering logic, training pipelines, and evaluation frameworks that improve scalability and consistency.
• Partner with data engineering and technology teams to make sure data pipelines, model inputs, and analytical datasets are production-ready and dependable.
• Create and assess generative AI solutions, including prompt-based workflows, retrieval-augmented generation, embedding pipelines, and other LLM-powered applications.
• Use Python, LLM APIs, LangChain or LangGraph, embeddings, vector search, RAG, and tool-calling patterns to develop reusable and governed AI components.
• Design test-and-learn experiments, pilots, control groups, and measurement frameworks to validate recommendations and quantify business impact.
• Support the rollout of machine learning and AI solutions into production or business-facing tools alongside data engineering and technology teams.
• Track model accuracy, stability, drift, and business outcomes, and identify when retraining, tuning, or enhancements are needed.
• Document model design, assumptions, limitations, performance results, and technical methods to support transparency and knowledge transfer.
• Ensure solutions are built and used responsibly, with attention to privacy, explainability, model risk, security, and governance.
• Keep up with new developments in data science, GenAI, and AI, and apply relevant innovations to CHANEL use cases.

Success Measures

• Solutions should create measurable business value, improve decisions, and support growth, client experience, or operational effectiveness.
• Models and analytical products should meet agreed standards for performance, reliability, explainability, and usability after deployment.
• High-value opportunities should be converted into prioritized and actionable data science or AI use cases with practical delivery plans.
• Outputs should be embedded into dashboards, data products, workflows, or decision processes and actively used by business teams.
• Reusable assets, frameworks, and AI components should reduce duplicate work and speed up future delivery.
• Stakeholders should see the work as relevant, clear, trustworthy, and impactful.
• No critical issues should arise from misuse, privacy gaps, security failures, governance breaches, or irresponsible AI practices.

What Energizes You

• Solving business problems through data and turning questions into structured analysis and practical recommendations.
• Building data science and AI solutions that produce measurable value and can be reused across multiple use cases.
• Exploring GenAI and agentic AI, then translating new capabilities into practical and responsible business applications.
• Learning quickly and adapting to the fast-moving AI landscape with curiosity and agility.
• Communicating analytical findings through clear data stories, visuals, and business implications that people can trust and act on.
• Working collaboratively with business, data engineering, technology, BI, analytics, and global teams to turn ideas into scalable solutions.

Capability Requirements

• Strong hands-on experience applying machine learning methods to real business problems.
• Advanced Python skills and solid experience with the data science ecosystem, plus practical use of deep learning frameworks such as PyTorch or TensorFlow.
• Working knowledge of Docker, Kubernetes, Git-based workflows, deployment, monitoring, retraining, and model lifecycle management.
• Exposure to GenAI methods such as prompt engineering, RAG, embeddings, vector databases, semantic search, or text analytics is advantageous.
• Experience with cloud AI and ML platforms such as Databricks, AWS SageMaker, or Azure Machine Learning, including scalable development and deployment.
• Familiarity with GenAI platforms and tools such as Azure AI Foundry, LangChain, or LangGraph is preferred.
• Comfort in designing pilots, A/B tests, control groups, and impact measurement frameworks.
• Understanding of Responsible AI principles, including privacy, security, explainability, bias, hallucination risk, human oversight, and governance controls.
• Experience using AI-assisted development tools such as Cursor, GitHub Copilot, or Claude Code as part of everyday workflows.
• Strong learning agility and the ability to quickly evaluate new AI capabilities and convert them into practical solutions.
• Business stakeholder communication skills, with the ability to explain technical ideas in clear and accessible language.

Inclusive Culture

CHANEL emphasizes an inclusive environment that supports personal growth and collective progress. The company values the individuality of every person and encourages applications from candidates who can bring new perspective, experience, and potential to the team.