ASTERRA is a successful earth observation company, based on Utilis technology, with headquarters in Israel and offices in the USA and UK. ASTERRA uses synthetic aperture radar (SAR) data from satellites and aircraft platforms to detect underground soil moisture, with applications for water utility and transportation infrastructures, as well as the property management industry. Come work at a place with true impact on the lives of millions.
The ASTERRA R&D department is seeking a highly capable and independent data scientist motivated to tackle hard problems as part of our Data Science team. The selected applicant will have the opportunity to make an impact using ASTERRA’s data on the company AI strategy and implementation, and a contribution to global sustainable development goals.
- Keep current of the State-of-the-Art in research and its application to SAR imagery.
- Contribute in all aspects of utilization of SAR imagery: Acquisition, pre-processing and transformation, sensor fusion, classification and detection, segmentation, change detection, and so on.
- Take ownership of Machine Learning solutions to some of our breakthrough products. Contribute as part of our product development squads.
- Strengthen our Machine Learning practice, continuously learn and share knowledge with your peers.
- Grow the impact that we bring in the various industries in which we operate, contributing to the scientific community.
- Expertise in machine learning, and deep learning in particular.
- Expertise in computer vision applications, and multidimensional imagery in particular.
- Expertise in Python programming and SQL scripting.
- Experience working with developer tools such as Git and Docker.
- Experience conducting applied research.
- Experience working both independently and as part of a team.
- Advanced academic degree in a relevant field in the sciences or engineering.
- Experience working with cloud computing environments such as AWS
- Experience working with geospatial databases such as PostGIS, and geospatial libraries such as GDAL, GeoPandas, and Rasterio.