15 of 15 GeoPandas Jobs

and/or SAR sensing phenomenologies and their unique characteristics Strong programming skills in Python or R, with experience using geospatial libraries (e.g., GDAL, GeoPandas) Working knowledge of computer vision techniques applied to satellite/aerial imagery Proficiency with geospatial metadata standards and their importance in analysis Experience evaluating dataset More ❯

for time series, spatial, and network data. Excellent programming skills in common languages (e.g., Python) and packages used by the energy modeling field (e.g., geopandas, numpy, networkx, pandas), use of software best practices (e.g., Git), and familiarity with high-performance computing environments. Experience with extracting, transforming, and loading processes and More ❯

for time series, spatial, and network data. Excellent programming skills in common languages (e.g., Python) and packages used by the energy modeling field (e.g., geopandas, numpy, networkx, pandas), use of software best practices (e.g., Git), and familiarity with high-performance computing environments. Experience with extracting, transforming, and loading processes and More ❯

formats such as GRIB, NetCDF, and HDF Proficiency in Python (3-5 years preferred) and familiarity with scientific computing libraries such as Pandas/Geopandas, NumPy, Xarray/Rioxarray, Raterio, Pysal, Pyproj, Shapely, PySpark etc Experience with AWS cloud services and infrastructure, with hands-on experience in data orchestration and More ❯

typically at a Senior Analyst or Analyst level role or external equivalent Experience in the following Technical Stack: Data Pipeline Stack: Python 3, pandas, GeoPandas, boto3, Pydantic, Data Version Control (DVC) 2, Core API Stack: Python 3, Django 4, Infrastructure: AWS, EKS, Docker, ADO (for CI/CD pipelines, git More ❯

office in Cornwall, UK. •Experience supporting machine learning workflows, especially for large model training. •Familiarity with handling geospatial datasets and related libraries (e.g., GDAL, GeoPandas, Rasterio). •Familiarity with data cataloguing tools and practices. •Prior experience in a startup or high-growth tech company. •Familiarity with containerisation (Docker), orchestration tools More ❯

in Geography, Environmental Science, Computer Science, GIS or a related discipline. Experience in geospatial data analysis and management using tools such as Python (e.g. GeoPandas, Rasterio, Xarray), GDAL, QGIS, and geospatial databases (e.g. PostGIS) Experience in designing, developing and maintaining automated data workflows that implement best practices of geospatial data More ❯

in Geography, Environmental Science, Computer Science, GIS or a related discipline. Experience in geospatial data analysis and management using tools such as Python (e.g. GeoPandas, Rasterio, Xarray), GDAL, QGIS, and geospatial databases (e.g. PostGIS) Experience in designing, developing and maintaining automated data workflows that implement best practices of geospatial data More ❯

Proven experience supporting secure data environments and cross-domain data transfer. Strong command of Python and familiarity with geospatial data libraries (e.g., GDAL, rasterio, geopandas). Excellent organizational and communication skills with a track record of collaboration across technical and non-technical teams. US Citizenship Active Secret clearance with eligibility More ❯

Proven experience supporting secure data environments and cross-domain data transfer. Strong command of Python and familiarity with geospatial data libraries (e.g., GDAL, rasterio, geopandas). Excellent organizational and communication skills with a track record of collaboration across technical and non-technical teams. US Citizenship Active Secret clearance with eligibility More ❯

and vector datasets, including Earth Observation or climate data. Proficiency with processing large, complex datasets using Python-based geospatial tools (xarray, numpy, scipy, dask, geopandas, OGR, GDAL). Strong quantitative skills with the ability to extract insights from spatial data. Familiarity with modelling concepts from hydrology, atmospheric sciences, or environmental More ❯

broad range of ML problems. Our Machine Learning tech stack includes: Python, ML libraries (TensorFlow, PyTorch, scikit-learn, transformers, XGBoost, ResNet), geospatial libraries (shapely, geopandas, rasterio), CV libraries (scikit-image, OpenCV, YOLO, Detectron2) AWS, Postgres, Apache Airflow, Kafka, Spark Mandatory requirements: At least 5 years of experience in data science More ❯

to ensure accuracy in manipulating, analysing, and presenting data, with excellent attention to detail Strong background in statistics or econometrics Spatial data statistics (e.g. Geopandas/Open Street Map) Knowledge of python or R desirable Personal skills suited to working within a professional yet friendly and dynamic team environment Self More ❯

structured and unstructured data, applying statistical techniques, and reporting results. Experience with geospatial data analysis tools and frameworks, such as ArcGIS, QGIS, GDAL, or GeoPandas, and knowledge of geospatial data formats, standards, protocols, and methods. Proficiency in data science experimentation methods, such as cross validation, regularization, encoding, or activation functions. … structured and unstructured data, applying statistical techniques, and reporting results. Experience with geospatial data analysis tools and frameworks, such as ArcGIS, QGIS, GDAL, or GeoPandas, and knowledge of geospatial data formats, standards, protocols, and methods. Proficiency in data science experimentation methods, such as cross validation, regularization, encoding, or activation functions. More ❯

Design, develop, and maintain Python applications, primarily focused on data manipulation and processing. Work with geospatial data using Python libraries such as Pandas and GeoPandas for advanced data analysis and transformation. Develop and maintain RESTful APIs using FastAPI to handle data processing and interaction with other services. Manage and process … or more of the following: Experience in working with geospatial data and Geographic Information Systems (GIS) . Experience using libraries such as Pandas and GeoPandas . Building APIs using FastAPI . Experience in setting up CI/CD pipelines in GitLab . Hands-on experience with Docker and docker-compose More ❯