Geospatial Applied Scientist

Job description

The Solution Development Department at Synspective is responsible for developing models and algorithms which produce insights using multiple sources of data, including our own satellite data. To do this, we develop an analytics platform to produce geoscience insights efficiently and easily. Responsibility Understand and organize customer needs from the internal business teams, partners, and customers. Develop and improve analytics models using StriX and other data. Support communicating analytics with the stakeholders. Details of work Research algorithms and develop code to extract insights from SAR imagery. Determine a suitable approach for each problem based on literature review and trials. Various tools can be utilized such as image processing, statistical learning, deep learning, etc Enrich your models by considering other types of information alongside SAR images (optical satellite, infrastructure polygons, weather data, etc) Closely collaborate with other scientists and also business members to find suitable solutions to various requests or issues. Utilize version control systems like Git to manage software code and collaborate with team members efficiently. Help finding and developing suitable visualization for various models and solutions. Continuously improve your skills and those of your team. Selling points of this role Work on a daily-basis on imagery produced by Synspective's satellite constellation Collaborate alongside highly skilled engineers and scientists in a global setting. Participate in the development of state-of-the art approaches solving concrete issues related to sustainability, disaster prevention and management, etc. Some freedom is given to employees on the nature of their tasks and how they solve the various issues assigned to them. Synspective provides support for learning new skills and passing various certifications Bachelor’s Degree in Data Science, Statistics, Computer Science, or related field. Production-ready proficiency in Python, including the scientific Python stack (NumPy/Pandas/Scikit-learn/SciPy). Familiarity with common raster and vector data formats, and hand-on experience with GDAL and Python geospatial libraries (rasterio, GeoPandas, shapely). Experience building analytics with geospatial data. Preferred qualifications Master’s Degree in Data Science, Statistics, Computer Science, or related field. Experience with SAR data, pre-processing and utilizing it in earth observation applications. Strong foundations in both classical ML and deep-learning, with hands-on experience with pytorch and/or other ML frameworks. Experience applying computer vision/ML methods to geospatial data. Fluent oral and written communication skills in Japanese to convey techniques and results of analyses clearly to both experts and non-experts.