Data Scientist – Deep Learning

at  GAF AG

GAF AG is an internationally successfully operating company for cross-platform EO-based geoinformation solutions and for the development of innovative GIS and database applications. In addition to the reception and distribution of geodata, GAF holds a leading position in the areas of data refinement and geoconsulting due to its many years of expertise. With an international and interdisciplinary team of experts, GAF sets standards for individual consulting as well as for state-of-the-art cloud- and AI-based solutions in the field of satellite-based monitoring of future- and sustainability-oriented application areas.
To further strengthen our team in the field of geodata analysis at our headquarters in Munich, we are looking for a

CORE REQUIREMENTS - THERE IS NO WAY AROUND IT:

• A master’s degree in science or engineering, such as geoinformatics, remote sensing, computer science, data science or similar
• 5 years of professional experience developing deep learning models for image segmentation and object detection
• Extensive experience with Python especially image processing in Python
• Hands-on experience with deep learning libraries such as TensorFlow, Keras and PyTorch
• Experience in building an infrastructure for training deep learning models with TensorBoard visualisations and binary data input pipelines
• Very good spoken and written English skills
• Ability to think out of the box and work solution orientated in a cross functional team with a broad variety of technical skills within an open and creative work environment

• Development of deep learning and computer vision methodologies for deriving information from satellite, climate and other geospatial data
• Improve our deep learning infrastructure used in our team and with customers
• Support your DevOps colleagues deploying code in different cloud environments and monitor/tune the applications for best performance
• Evaluating and testing new technologies, frameworks or libraries and assessing them in terms of their benefit for the company
• Extending existing deep learning architectures, tailored to project requirements. Contributing your own ideas and suggestions for improvement or new developments