6 of 6 GeoPandas Jobs in the UK

classification.Strong SQL skills and experience working with relational databases.Sound understanding of statistics, predictive modelling and machine learning techniques.Experience working with geospatial datasets using GeoPandas, QGIS or similar technologies.Ability to work with large and complex datasets and build robust data pipelines.Excellent analytical and problem-solving skills with the ability to communicate ...

driven services, and message-driven patterns. Containerisation: experience using Docker and deploying containerised applications Nice to have: GIS & point-cloud processing: GDAL/PDAL, GeoPandas, Open3D, LAS/LAZ/Raster/Vectorhandling, spatial transforms and analysis. TypeScript/JavaScript for developing front-end dashboards and user interfaces. Packaging & distribution ...

time-series problems.• Commodity trading exposure or understanding of how maritime data drives commodity markets.• Containerization— Docker/Kubernetes for reproducible, deployable pipelines.• GeoPandas— for geospatial data manipulation and analysis in Python.• Domain knowledge of shipping— vessel classes, charter markets, freight rates, and IMO regulations. ...

demonstrate...Strong professional experience in GIS, with a clear focus on Python development and automation.Strong Python skills: ArcPy, ArcGIS API for Python, geopandas, shapely, rasterio, GDAL.Practical experience applying AI/ML to geospatial problems (e.g. object detection, image classification, feature extraction, or spatial prediction).Working knowledge of at least one deep ...

investor materialsWhat We’re Looking ForStrong Python skills (pandas, NumPy, scikit-learn, matplotlib)Experience with SQL and relational databasesExposure to geospatial tools (QGIS, GeoPandas, or similar)Solid understanding of statistics and machine learning fundamentalsInterest in renewable energy, power systems, or infrastructureExperience with data visualisation tools (Power BI, Plotly, Streamlit ...

Desirable Skills Experience with geospatial technologies and concepts, including: Spatial databases (e.g., PostGIS, BigQuery, DuckDB, Google Earth Engine) Geospatial libraries (e.g., GDAL/OGR, GeoPandas, Proj4, Gmaps, Leaflet, Leafmap,rasterio, STAC) Geospatial data formats (ESRI shapefiles, geojson, geotiff, zarr, geoparquet Familiarity with data science libraries like pandas. Geopandas, xarray ...