About this role
Summary: Lead technical efforts in the Agro domain using Python and geospatial data science. Responsibilities include developing production-quality code, managing vector and raster data, and translating agronomic concepts into data features for predictive modeling.
Job Summary
Python Developer / Data Scientist for ADS
Geospatial
- Strong with the Python geospatial stack: GeoPandas, Shapely, Rasterio, pyproj, Fiona
- Solid grasp of coordinate reference systems (CRS), projections, and reprojection pitfalls
- Vector and raster handling: polygons, field boundaries, GeoTIFFs, zonal statistics, clipping, masking, resampling
- Spatial joins, overlays, and intersection logic at scale (e.g., field polygon vs. land-cover raster)
- Familiarity with common formats: GeoJSON, Shapefile, GeoPackage, Cloud-Optimized GeoTIFF (COG), Parquet/GeoParquet
Key Responsibilities
Agro domain
- Understanding of crop types, growing seasons and crop calendars
- Knowledge of key agronomic risk drivers: drought, flood, frost, heat stress, hail, pests/disease
- Familiarity with agricultural data sources: land-cover and crop-type products (e.g., MapBiomas), yield statistics, soil data
- Ability to translate agronomic concepts into data features (e.g., NDVI anomalies, precipitation deficits, growing degree days)
Skill Requirements
Python and data science core
- Production-quality Python: clean, tested, typed, maintainable code.
- SQL, ideally PostgreSQL