Geospatial Data Scientist

orcristtechnologies; United States; Remote; USD 140,000 - 180,000; Full time

Benefits offered by this job

Remote‑first work in Germany

30 days vacation

Learning support

Job summary

orcristtechnologies is seeking a geospatial data scientist to develop methods for analyzing satellite imagery and other spatial data. The role emphasizes Python-based workflows across change detection, object detection, segmentation, and spatial-temporal analysis.

You will work with optical, multispectral, thermal, and SAR data, evaluating methods for production readiness and scalability in a remote-first setting with Germany-based teams.

Qualifications

• Formal university training in geoinformatics, remote sensing, Earth observation, or related discipline.
• Strong Python and scientific computing skills using NumPy, pandas, SciPy, xarray, GeoPandas, Rasterio/GDAL.
• Practical machine‑learning experience with scikit‑learn and PyTorch or equivalent; TorchGeo or geospatial DL packages.
• Solid understanding of remote‑sensing fundamentals including spatial, spectral, radiometric, and temporal resolution.
• Experience with satellite image analysis and at least one task such as change detection, segmentation, object detection, or land‑cover classification.
• Clear communication in English and eligibility to work in Germany.

Responsibilities

• Develop change-detection workflows over open satellite time series.
• Build and evaluate object-detection, segmentation, and classification methods for satellite imagery.
• Assess suitability of sensors, resolutions, acquisition conditions, and processing levels across optical, multispectral, thermal, and SAR data.
• Design preprocessing and feature extraction with data engineers, including masking, compositing, co-registration, normalization, and spectral indices.
• Combine raster outputs with vector and temporal data for spatial statistics, zonal analysis, and anomaly detection.
• Build or source reference datasets and evaluation protocols with cross-regional validation.
• Package tested Python methods for batch processing or inference and integrate into platform workflows.

Skills

Geospatial analysis

Remote sensing

Machine learning

Python programming

Education

Geoinformatics/related field

Tools

Python

NumPy

pandas

SciPy

xarray

GeoPandas

Rasterio

GDAL

PyTorch

TorchGeo

Job description

Role overview

Develop geospatial and remote-sensing methods that turn imagery and spatial data into reliable analytical products within an existing intelligence platform. The role works primarily in Python across change detection, object detection, segmentation, and spatial-temporal analysis, spanning classical machine learning, statistical and image analysis, and deep learning approaches such as Vision Transformers and U-Nets.

Responsibilities

• Develop change-detection workflows over open satellite time series, distinguishing 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, weighing modern deep learning approaches against simpler baselines in terms of accuracy, label requirements, generalization, inference cost, and production viability.
• Assess suitability of different sensors, resolutions, acquisition conditions, and processing levels, extending methods across optical, multispectral, thermal, and SAR data as requirements develop.
• Design preprocessing and feature extraction together with data engineers, including 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, including spatially and temporally separated validation, false-positive and missed-detection measurement, and performance checks across regions and sensors.
• Package tested Python methods for repeatable batch processing or inference and collaborate on runtime, memory, monitoring, and integration into platform workflows.

Requirements

• 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 NumPy, pandas, SciPy, xarray, GeoPandas, and Rasterio/GDAL.
• Practical machine‑learning experience with scikit‑learn and PyTorch or an equivalent framework, plus TorchGeo or related geospatial deep learning packages.
• Solid understanding of remote‑sensing fundamentals including 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; clear communication in English and eligibility to work in Germany.

Nice to have

• Experience with thermal infrared imagery, SAR data processing and analysis, or combining observations from multiple sensors; a PhD in a relevant field.
• Experience with geospatial foundation models such as Prithvi, TerraMind, or AlphaEarth Foundations embeddings and evaluating when they help over task‑specific models.
• Scaling geospatial analysis with Dask, Apache Spark/Sedona, or Zarr; deploying and optimizing GPU inference with PyTorch/CUDA, ONNX Runtime with TensorRT, or NVIDIA Triton.
• Spatial SQL with DuckDB or PostGIS, STAC‑based data discovery, and exploratory work in QGIS or geemap.

Benefits and work setup

• Remote‑first work in Germany with regular team sessions in Berlin and occasional sessions in Frankfurt and Munich, 30 days of vacation, equipment and learning support, and room to develop expertise across remote sensing and geospatial analysis.