Spatial Data Scientist - Machine Learning & Remote Sensing

The Center for International Forestry Research and World Agroforestry (CIFOR-ICRAF), a CGIAR research center, seeks a Spatial Data Scientist to lead and implement advanced remote sensing analysis tasks supporting global research programs on forests, trees, and agroforestry. Responsibilities include designing machine learning pipelines for remote sensing applications, processing optical (Sentinel 2, Landsat 8/9) and SAR (Sentinel 1) data, developing deep learning models (CNNs, RNNs, LSTMs, Transformers), implementing time series analysis for vegetation and climate monitoring, building automated data processing pipelines using Python and Google Earth Engine, analyzing deforestation patterns, creating interactive web mapping applications, supervising junior data scientists, and conducting capacity development workshops. Supports programs including Regreening Africa, Knowledge for Great Green Wall Action, and assessments of soil and land health. Requires PhD or MSc in spatial data science, geoinformatics, computer science, or related field with ML/AI expertise, advanced Python/R proficiency, deep learning framework experience (TensorFlow, PyTorch, Keras), and cloud platform skills (GEE, AWS, GCP). Two-year appointment with competitive USD salary, based in Nairobi, Kenya or Remote. Closing date: April 3, 2026.