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, InSAR, 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 InSAR tools and chainImplement new functionalities and features to expand processing capabilities and product offeringsDesign and deploy machine learning models, including convolutional neural networks (CNNs), for tasks such as phase unwrapping, atmospheric correction, anomaly/deformation detection, and signal classificationWork with large volumes of SAR and interferometric data, building robust and scalable pipelinesCollaborate closely with geophysicists, radar engineers,



and software developers to translate research into production-ready toolsValidate model outputs against ground truth and domain expertise; iterate based on real-world performanceContribute to technical documentation and internal knowledge sharingWhat We're Looking For :Degree (MSc/PhD) in Data Science, Computer Science, Geophysics, Remote Sensing, Applied Mathematics, or a related fieldExperience in machine learning, with hands-on expertise in convolutional neural networks (CNNs) and deep learning frameworks (PyTorch, TensorFlow)Strong Python programming skills; experience with scientific computing libraries (NumPy, SciPy, xarray, etc.)Experience working with geospatial or remote sensing data (SAR/InSAR experience is a strong plus, but not mandatory)Familiarity with signal/image processing techniquesComfortable working with large datasets and building production-grade data pipelinesStrong analytical mind

📌 Data Scientist F/M (Barcelona)
🏢 VINCI Group
📍 Barcelona