Data Scientist Senior F/H

Description

Integrated into the Data & AI Hub team, you will join a young team of Data Scientists, Data Engineers, and ML Engineers.

You will work on three main missions:

• Internally, you will participate in defining the optimal data strategy for our organization (structuring, processes, open data, external data purchases).
• In project mode, you will participate in the end-to-end management of final projects: data collection, preprocessing pipeline, modeling, and deployment. You will contribute to the development of the group's four main axes:
• Inspection 4.0: development of computer vision and NLP algorithms to support field experts.
• Personalized agents for group services (RAG, text-to-SQL, etc.).
• IA+X for new services: development of hybridization techniques between physics and AI (digital twin, PINN).
• Information extraction from documents and plans.
• Mentoring junior teams and animating the Data & AI expert community.

The work will be done in collaboration with a team of developers to develop models and host deliverables on an AWS web platform.

At our organization, careers are built with you towards what suits you best: technical expertise, team management (lead data), etc.

You will have the opportunity to interact internationally (US, UK, ITA, ESP, NL) and international mobility opportunities are possible.

Qualifications

• Master's in Data Science / Machine Learning or Generalist Engineer with a passion for data.
• Minimum 5 years of experience in applied Data Science, ideally in an industrial, technical, or data-intensive environment.
• Proficiency in Python and its data ecosystem (pandas, NumPy, scikit-learn, matplotlib, seaborn, etc.).
• Solid experience with machine learning and deep learning algorithms, as well as at least one reference framework (TensorFlow, Keras, or PyTorch).
• Mastery of collaborative development tools: Git, GitLab, Jupyter Notebooks, VS Code, etc.
• Knowledge of AWS environments (Lambda, Timescale, S3) and best practices for deploying models in production (MLOps) appreciated.
• Knowledge of statistics, modeling, and multivariate analyses (factor analysis, PCA, clustering, regressions, etc.).
• Versatility and autonomy throughout the data project lifecycle (exploration, modeling, validation, deployment).
• Team spirit, scientific rigor, and a sense of collaboration.
• Ability to support business and technical teams, simplify complex topics, and contribute to structuring good data practices.
• Fluent in English, both spoken and technical.