Geospatial Data Scientist

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Description

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. Role 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. The work includes open satellite time series and commercial optical, thermal, and SAR imagery. You'll help choose appropriate data and methods, establish what the results can support, and work with engineers to make them available to analysts inside Sentinel. What you'll do 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 imag

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