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 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 sharingWhat We're Looking For :- Degree (MSc/PhD) 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 (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 techniques- Comfortable working with large datasets and building production-grade data pipel

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