Geospatial & Climate Data Intern - Python

Position - Geospatial & Climate Data Intern - Python

Location: New Delhi or Remote (India)

Type: Internship (3 months)

Stipend - 15k (negotiable)

About the Role

We are looking for an intern who is comfortable wrangling geospatial and climate datasets, consuming APIs, and building clean, reproducible Python workflows. You’ll support climate‑risk and ESG analytics: downloading reanalysis/satellite datasets, preprocessing them into analysis‑ready layers, computing indices, and packaging outputs for dashboards and models.

Responsibilities

·      Ingest climate and geospatial datasets (NetCDF, GRIB, GeoTIFF, shapefiles, Parquet, Zarr/COGs) from public APIs and cloud buckets; organize them with clear metadata and folder conventions.

·      Build Python pipelines for data cleaning, QA/QC, spatial joins, projections, masking, resampling, mosaicking, clipping, and tiling.

·      Regrid, aggregate, and compute climate indices and exposure overlays against administrative boundaries and customer assets.

·      Automate retrieval of reanalysis/forecast products, satellite products and hydromet data; cache and catalog with STAC‑like metadata where possible.

·      Produce quick‑turn visualizations and summary tables for internal stakeholders and client deliverables.

Core Tech Stack

·      Data & Arrays: numpy, pandas, xarray, dask, zarr, cftime, xhistogram

·      Climate/Met: xclim (climate indices), xesmf (regridding), cfgrib/eccodes, netCDF4, metpy

·      Geospatial: geopandas, rasterio, rioxarray, shapely, pyproj, rasterstats, pyogrio

·      Visualization/Maps: matplotlib, cartopy, plotly, folium

Qualifications

·      Strong Python skills and familiarity with notebooks (Jupyter) and script‑first workflows.

·      Hands‑on with at least 3 of: xarray, geopandas, rasterio, rioxarray, xclim, netCDF4, cfgrib.

·      Comfortable with coordinate reference systems, projections, and raster‑vector operations.

·      Experience consuming REST/JSON APIs; ability to handle pagination, retries, and rate limits.

·      Basic Git (branching, PRs) and environment management (conda/mamba or poetry).

What you will learn

·      End‑to‑end climate/EO data engineering: from ingestion to analytics to visualization.

·      Practical climate‑risk metrics (heat, flood, drought, wind) and ESG analytics.

·      Modern geospatial & array computing at scale with xarray + dask.

Application

Send your CV, GitHub/portfolio, and a short note on one geospatial/climate project you’ve built on careers@endecarb.ai