Geospatial Data Scientist
New
O
OrcristDefense / Intelligence
Remote-first work in Germany with regular team sessions in Berlin and occasional sessions in Frankfurt and Munich.Full-TimeMiddle
Salary not disclosed
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Job Details
- Languages
- English
- Required Skills
- PythonNumpyPyTorchPandasscikit-learn
Requirements
- Formal university training in geoinformatics, remote sensing, Earth observation, applied mathematics, computer science, or a related discipline.
- Substantial hands-on geospatial or remote-sensing experience.
- Strong Python skills using libraries such as NumPy, pandas, SciPy, xarray, GeoPandas, and Rasterio/GDAL.
- Practical machine-learning experience with scikit-learn and PyTorch or equivalent.
- Experience with TorchGeo or related geospatial deep learning packages.
- Solid understanding of remote-sensing fundamentals including spatial, spectral, radiometric, and temporal resolution, and coordinate systems.
- Experience with satellite image analysis (change detection, segmentation, object detection, or land-cover classification).
- Sound statistical judgment regarding sampling, spatial autocorrelation, class imbalance, and generalization.
- Engineering-minded approach to research with reproducible, versioned code and experiments.
- Clear communication in English with both technical colleagues and domain specialists.
Responsibilities
- Develop change-detection workflows, including Sentinel-2 time series, and distinguish meaningful change from seasonality, cloud and shadow effects, acquisition differences, and registration errors.
- Build and evaluate object-detection, segmentation, and classification methods for satellite imagery, using statistical techniques, classical image processing, and machine learning where appropriate.
- Assess the suitability of different sensors, resolutions, acquisition conditions, and processing levels for each use case.
- Design preprocessing and feature extraction with data engineers: quality masking, compositing, co-registration, normalization, spectral indices, and sensor-specific corrections.
- Combine raster outputs with vector and temporal data for spatial statistics, zonal analysis, anomaly detection, and comparison across areas and observation periods.
- Build or source reference datasets and evaluation protocols, measuring performance across regions and sensors.
- Deliver traceable results with source references, timestamps, confidence or uncertainty measures, and documented limitations.
- Package tested Python methods for repeatable batch processing or integration into Sentinel workflows.
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