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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

Data Scientist - Deep Learning with focus on remote sensing for the processing and analysis of geospatial data

The results of our work are used by authorities, companies and in science worldwide for further analysis and decision-making. Using our data analysis capabilities, we provide thematic and statistical information at various spatial reference levels locally and globally. Processing large volumes of satellite data with innovative analysis methods in cloud infrastructures has been indispensable for several years and enables, for example, the creation of land cover products on a continental scale as part of the EU‘s Copernicus program


Aufgaben
  • 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

Profil
  • 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

Desirable and advantageous, but not a prerequisite:

  • Experience with remote sensing and geodata
  • Hands-on experience bringing deep learning models models in production using technologies like TensorFlow Serving, MLFlow, Docker with GPU support and ONNX on Windows and Linux
  • Experience with data processing and machine learning libraries like Scikit-Learn, Pandas and Numpy

Wir bieten
  • Content-wise diverse, technically demanding and responsible tasks in one of Europe's leading geo-information companies
  • Comprehensive support when you start at GAF, as part of our mentoring programme
  • Training and development – because your experience and knowledge count
  • A respectful, friendly and modern project working environment in a multi-national team of committed colleagues and a leadership style based on trust
  • Flexible working hours and the opportunity to work from home within the framework of our regulations
  • Workation: temporary work from abroad depending on individual and legal requirements
  • Contribution to the Deutschland-Ticket (job ticket), an attractive location with perfect public transport connections and great options for outdoor and recreational activities in the direct vicinity
  • Various additional benefits, e.g. company pension scheme (optional), travel health insurance (worldwide), occupational health management, and much more