Geospatial Data Scientist

Orcrist is building a next generation data intelligence platform using cutting-edge technologies. We're handling petabyte-scale data with sub-second queries. Our product is a Kubernetes‑based platform delivered as B2B SaaS or as a self‑hosted on‑prem solution, including air‑gapped deployments. We enable customers across defense, law enforcement, and enterprise to turn mission-critical data into actionable intelligence.

Develop geospatial and remote-sensing methods that turn imagery and spatial data into reliable analytical products. You'll work in Python on change detection, object detection, segmentation, and spatial and temporal analysis. Your methods will span classical machine learning, statistical and image analysis, and deep learning architectures such as Vision Transformers (ViTs) and U-Nets, taking the approaches that prove useful from exploration through evaluation into repeatable platform capabilities.

• 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. Evaluate state-of-the-art deep learning approaches pragmatically against simpler baselines, weighing accuracy, label requirements, generalization, inference cost, and real production viability.
• Assess the suitability of different sensors, resolutions, acquisition conditions, and processing levels for each use case. Extend methods across optical, multispectral, thermal, and SAR data as requirements develop.
• 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. Use spatially and temporally separated validation, measure false positives and missed detections, and examine performance across regions and sensors.
• Deliver traceable results with source references, timestamps, confidence or uncertainty measures, and documented limitations. Help analysts understand when a result needs closer review.
• Package tested Python methods for repeatable batch processing or inference. Work with data and platform engineers on runtime, memory, monitoring, and integration into Sentinel workflows.

• Formal university training in geoinformatics, remote sensing, Earth observation, applied mathematics, computer science, or a related discipline, combined with substantial hands‑on geospatial or remote‑sensing experience.
• Strong Python and scientific computing skills, using tools such as Num Py, pandas, Sci Py, xarray, Geo Pandas, and Rasterio/GDAL.
• Practical machine‑learning experience with scikit‑learn and PyTorch or an equivalent framework, plus Torch Geo or related geospatial deep learning packages, including training, evaluation, and adapting existing models.
• A solid understanding of remote‑sensing fundamentals: spatial, spectral, radiometric, and temporal resolution; coordinate systems; image alignment; and data-quality limitations.
• Experience with satellite image analysis and at least one relevant task such as change detection, segmentation, object detection, or land‑cover classification.
• Sound statistical judgment around sampling, spatial autocorrelation, data leakage, class imbalance, uncertainty, and generalization to new places and acquisition…