About the Role : Sixense Satellite, a subsidiary of VINCI Construction, is a leading player in satellite-based ground monitoring. We develop advanced technologies combining remote sensing, In SAR, and data science to deliver high-impact decision-support solutions for infrastructure management, natural hazard monitoring, and land-use planning. As part of the VINCI Construction group, we help our clients better understand and anticipate ground deformation phenomena through Earth observation data and cutting-edge technologies. What You'll Do : Contribute to the design, development, and optimization of our In SAR tools and chain Implement new functionalities and features to expand processing capabilities and product offerings Design and deploy machine learning models, including convolutional neural networks (CNNs), for tasks such as phase unwrapping, atmospheric correction, anomaly/deformation detection, and signal classification Work with large volumes of SAR and interferometric data, building robust and scalable pipelines Collaborate closely with geophysicists, radar engineers,



and software developers to translate research into production-ready tools Validate model outputs against ground truth and domain expertise; iterate based on real-world performance Contribute to technical documentation and internal knowledge sharing What We're Looking For : Degree (MSc/Ph D) in Data Science, Computer Science, Geophysics, Remote Sensing, Applied Mathematics, or a related field Experience in machine learning, with hands-on expertise in convolutional neural networks (CNNs) and deep learning frameworks (Py Torch, Tensor Flow) Strong Python programming skills; experience with scientific computing libraries (Num Py, Sci Py, xarray, etc.) Experience working with geospatial or remote sensing data (SAR/In SAR experience is a strong plus, but not mandatory) Familiarity with signal/image processing techniques Comfortable working with large datasets and building production-grade data pipeli

📌 Data scientist f/m (Barcelona)
🏢 VINCI Group
📍 Barcelona