Stagiaire Data Scientist (F/H/X)

Description du poste

Join CHANEL's Data Science team in luxury watchmaking with AI solutions. Experience a collaborative and innovative work environment. Tasks Contribute to machine learning and generative AI projects. Engage in computer vision and model optimization tasks. Develop AI solutions through all project life cycle phases. Skills Final-year student in applied mathematics, computer science, or related fields. Proficiency in Python and machine learning concepts. Familiarity with cloud computing and MLOps practices. This job ad was AI-translated from French. See original About the job Within the Data Science team of CHANEL's Watchmaking and Jewellery division, you will contribute to the development of artificial intelligence solutions used in real and high-impact contexts. You will join a technical team working on various issues related to machine learning, generative artificial intelligence, computer vision, optimisation and model industrialisation. The internship offers a unique opportunity to be involved in the entire lifecycle of an AI project: understanding the problem, data exploration, prototyping, model development, deployment and production monitoring. You will be directly involved in technical decisions and will have the opportunity to work on projects whose results are used by the operational teams of the House. The goal of the internship is for you to gain concrete experience in developing AI solutions in production, from initial experiments to deployment, while being exposed to a variety of technical challenges and technologies that are now at the heart of the industry: LLM, computer vision, optimisation, cloud and MLOps. What will you be working on? The topics evolve constantly according to the team’s needs and the intern’s interests. A significant part of our activity is dedicated to generative AI. We notably develop specialised assistants based on RAG (Retrieval-Augmented Generation) architectures. The team also works on advanced computer vision issues, which lead us to use modern deep learning architectures, siamese networks, as well as synthetic data generation techniques. Other projects focus on forecasting, statistical modelling and decision support. We develop models intended to anticipate certain operational phenomena, detect atypical behaviours or improve the quality of decisions made by the teams. Finally, we regularly explore approaches from operational research and mathematical optimisation to solve complex problems of resource allocation, planning or industrial sourcing. Particular attention is paid to industrialisation. The models developed do not remain prototypes but are integrated into cloud environments relying on modern software engineering and MLOps practices. Technical environment The team uses the Python ecosystem daily for developing machine learning and deep learning models. Depending on the projects, you may work with PyTorch, Scikit-Learn, Yolo, Langchain, as well as various optimisation and operational research tools. On the engineering side, we place great importance on software quality and reproducibility of work. Development notably relies on Git, Docker, CI/CD practices, Azure and Databricks environments, as well as MLOps principles allowing deployment, monitoring and maintenance of models over time. Profile sought You are in your final year of studies in a course focused on applied mathematics, computer science, artificial intelligence, data science or an equivalent discipline. You already have a good command of Python and solid foundations in statistics, machine learning and software development. Experience with deep learning, LLM, computer vision, operational research, cloud computing, Git, Docker, Agile, DevOps or MLOps will be an asset. Beyond technical skills, we are primarily looking for curious, rigorous and autonomous profiles, capable of tackling open problems, proposing innovative solutions and transforming theoretical concepts into concrete applications. Position conditions: An internship agreement is mandatory for this position and must meet the following conditions: The internship must be part of your study programme. The internship must be necessary for obtaining the degree. The internship must be validated by the awarding of study credits. Position information: Contract: full-time internship (100%) and on-site Start: March 2027 Duration: 6 months Working days: 5 days per week, Monday to Friday Location: Geneva Fluent English