Project summary

• Design a parametric crop insurance index tailored for smallholder farmers using satellite-derived indicators and rainfall data.
• Use remote sensing time series to build robust, scalable indices that can be automated for large areas.

Objectives & main tasks

• Extract and preprocess satellite and rainfall datasets (NDVI from MODIS/Sentinel-2 via Google Earth Engine, CHIRPS rainfall).
• Define candidate parametric index formulations, calibrate thresholds and loss triggers suitable for smallholder contexts and validate against ground truth or proxy yield data.

Technical environment & tools

• Languages & libraries: Python (pandas, numpy), R (optional), Scikit-learn for basic modeling.
• Platforms & tools: Google Earth Engine for satellite data extraction and processing, Jupyter Notebook for analysis, QGIS for spatial mapping.
• Storage & visualization: PostgreSQL/PostGIS for spatial data storage and Metabase or Dash/Plotly for interactive visualization.

Expected deliverables

• Cleaned datasets and reproducible extraction scripts (Google Earth Engine + ingestion to PostgreSQL/PostGIS).
• Analysis notebooks showing index formulation, calibration, validation results and simple models (scikit-learn).
• Spatial maps and interactive visualizations (QGIS exports and Dash/Plotly or Metabase dashboards) plus a final written report describing methodology and recommendations.

Skills & competencies sought

• Strong data processing and Python experience (pandas, numpy); familiarity with remote sensing concepts (NDVI, time-series) and Google Earth Engine.
• Experience with spatial databases (PostGIS), basic machine learning for index calibration, and dashboarding/visualization tools.

How to apply

• Apply via the company site: https://www.hydatis.com
• Or send your application email to stages@hydatis.fr including CV, cover letter, and a short description of relevant projects (use the subject line specified below).