Aymn Elhaddad

Background

About Aymn Elhaddad

I am a data scientist with over 15 years of experience converting Earth-observation and meteorological data into wildfire-hazard, vegetation-exposure, and environmental-risk products. I’m passionate about cloud-native geospatial systems, ML interpretability (SHAP, XGBoost), and bringing science into daily operational decision-making.My expertise spans remote sensing (MODIS, Landsat, LiDAR, SAR), evapotranspiration & drought modelling, vegetation-fuel mapping, and machine-learning forecasting (XGBoost, SHAP, ensembles). I’ve led 7-day wildfire-probability forecasting systems for utilities, built operational pipelines in Python and Google Earth Engine on AWS/AZURE, and collaborated across teams to deliver actionable intelligence for utilities, land-managers and emergency-response agencies.I’m now seeking senior/lead roles where I can apply my niche in wildfire/fuel analytics and geospatial ML to help organizations anticipate, monitor and mitigate vegetation-driven risk.Previously at Gro Intelligence, I co-created global fuel-dryness, drought, and evapotranspiration indices and delivered decision-ready hazard dashboards that informed stakeholders across agriculture, energy, and environmental-response sectors.I’m passionate about bridging science and operations—translating geospatial ML models into tools that protect infrastructure, reduce PSPS impacts, and strengthen community wildfire resilience.Keywords Wildfire Modeling • DFMC / LFMC • Vegetation Fuels Mapping • Hazard Forecasting • PSPS Analytics • MODIS / NAIP / Landsat • GEE → AWS Migration • XGBoost / SHAP • Time-Series Forecasting • Geospatial Python • Utility Wildfire Mitigation.