in applied machine learning (regression, tree ensembles, experiment design). Advanced proficiency in Python and SQL, with experience in spatial SQL and PostGIS. Familiarity with spatial tools and libraries (GeoPandas, QGIS) and feature engineering concepts. Experience with data modelling, dbt, and version control (Git). Knowledge of spatial datasets (MasterMap, AddressBase, Land Registry). Desired: Experience with WMS/WFS More ❯
Git for version control and collaboration Experience in use of ML, NLP and Foundation Models in ETL pipelines Knowledge of secure coding principles Familiarity with geospatial libraries such as GeoPandas, Shapely, and GDAL Knowledge of PostgreSQL/PostGIS for spatial data management Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data science tools (e.g., Jupyter, Pandas, NumPy More ❯
in Python, with a strong adherence to Python best practices Experience using Git for version control and collaboration Knowledge of secure coding principles Expertise in geospatial libraries such as GeoPandas, Shapely, and GDAL Advanced knowledge of PostgreSQL/PostGIS for spatial data management Experience with AWS and Azure platforms, including AI services (e.g., AWS SageMaker, Azure ML) Proven experience developing More ❯
