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

Other Requirements