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 sharing

What 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 productio

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