Spatial Data Scientist – Machine Learning & Remote Sensing

This is a remote position.

• Design and implement machine learning pipelines for geospatial analysis, including feature engineering, model selection, hyper parameter tuning, and validation.
• Develop and deploy deep learning models (CNNs, RNNs, LSTMs, Transformers) for image classification, segmentation, object detection, and time series forecasting.
• Apply advanced AI techniques for predictive modelling and mapping of indicators relevant to ecosystem health assessment using field data and multi-source remote sensing.
• Process and analyze optical data (Sentinel 2, Landsat 8/9) and SAR data (Sentinel 1), including data fusion and feature extraction for ML workflows.
• Implement time series analysis and forecasting models, including trend detection, anomaly identification, and predictive analytics for vegetation, precipitation, and land surface dynamics.
• Develop scalable, reproducible spatial data processing workflows and contribute to MLOps practices.
• Supervise a team of junior spatial data scientists and developers. • Develop communication products/outputs where relevant.

• Lead internal capacity development seminars within CIFOR-ICRAF on machine learning, AI applications, and spatial data science.
• Capacity development of partners and stakeholders through workshops as part of projects with particular emphasis on ML-driven spatial analysis and modelling.

• Work closely with the CIFOR-ICRAF stakeholder engagement team (SHARED) to provide AI-driven analytical outputs that feed into project delivery, for example monitoring outputs as part of the Great Green Wall.
• Contribute to stakeholder engagement events as part of the development of decision support tools and platforms.

• Contribute to micro-dashboard development as part of the Global Resilience Impact Tracker platform
• Support projects and programs with analytical support and stakeholder engagement with decision makers.
• Lead and/or contribute to scientific papers.
• Contribute to proposal development and writing.

Requirements

• PhD or MSc degree in spatial data science, geoinformatics, computer science, or a related quantitative field with demonstrated expertise in machine learning and AI applications.
• Proven experience developing and deploying machine learning models for geospatial applications.
• Strong proficiency in deep learning frameworks (TensorFlow, PyTorch, Keras) and familiarity with architectures such as CNNs, RNNs, LSTMs, and Transformers.
• Advanced programming skills in Python and/or R Statistics; familiarity with Julia is a plus.
• Experience with cloud computing platforms (GEE, AWS, GCP) and big data processing tools for geospatial analysis.
• Knowledge of remote sensing data processing and analysis, including optical and SAR platforms.
• Excellent interpersonal skills.
• Excellent written and spoken English. Knowledge of French a plus.