13 of 13 Permanent GeoPandas Jobs

data science experience, including managing 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. Other Requirements: Ability to meet … data science experience, including managing 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. Other Requirements: Ability to meet More ❯

Demonstrable knowledge of EO, IR, 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 suitability for ML training and More ❯

wrangling and cleaning techniques, especially 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 tools for handling large-scale More ❯

and/or relevant experience, 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 hosting, ticket tracking),Hardware: Mac More ❯

from raw climate data in 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 deployment (S3, Lambda, Redshift, Glue More ❯

skills. • Proximity to our Penryn 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 (Airflow, Prefect), and CI/ More ❯

using cutting-edge technologies. Responsibilities: 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 Geographic Information Systems (GIS) data … to detail. Proficiency in one 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 for containerized application development and More ❯

Machine Learning community, tackling a 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, with deployment into production; Proven More ❯

these essential skills: A degree 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 management Demonstrable knowledge of software More ❯

development, preferably involving geospatial data. 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 for TS Desired Qualifications: Fully More ❯

development, preferably involving geospatial data. 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 for TS Desired Qualifications: Fully More ❯

working with large geospatial raster 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 sciences. Desirable Skills Experience with More ❯

Problem-solving skills Demonstrable capability 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-motivated with the ability to More ❯