Specialist in Agrometeorology, Early Warning, and Artificial Intelligence​/Machine Learning

Overview

Organizational Setting
The FAO Regional office for Asia and the Pacific (FAO-RAP) supports member countries in the development of knowledge products, capacity development and implementation of programmes and projects in food security, agriculture, rural development, natural resource management and agro-processing. It assists governments and country stakeholders, main development partners, regional and national institutions with policy and strategic planning to achieve food security and nutrition, make agriculture, forestry and fisheries more sustainable and productive, reduce rural poverty, make food value chains more efficient and inclusive and promote climate change adaptation and/or mitigation.

Under the FAO-RAP regional priority on “Accelerating sustainable natural resources management for biodiversity conservation and climate action,” FAO aims to support Members to protect, restore and promote sustainable use of terrestrial and aquatic ecosystems and combat climate change in realizing more efficient, inclusive, resilient and sustainable agri-food systems. This work is coordinated regionally by the Climate Change, Resilience, Land and Water Module (CRLWM), which leads technical quality assurance and coherence of FAO’s field programme in Asia and the Pacific.

Within this framework, the Specialist in Agrometeorology, Early Warning, and AI/ML will contribute to the design, technical backstopping, and implementation of projects and knowledge products that strengthen the use of agro-climate information, AI-driven predictive analytics, and early warning systems for agricultural risk management and anticipatory action. The position may be home-based with travel required for field missions across the region.

The Specialist in Agrometeorology, Early Warning, and AI/ML will work under the overall technical supervision of the FAO Emergency and Rehabilitation Officer, in close collaboration with the FAO Representation in the relevant country/ies of assignment.

This position focuses on advancing climate-resilient agriculture through the integration of agrometeorology, early warning systems, and AI/ML. The Specialist will apply specialized knowledge in agro-climatic forecasting, climate risk monitoring, and climate/crop interactions to strengthen disaster and climate change adaptation decision-making. They will design and optimize early warning models, harness AI/ML techniques for predictive analytics, and ensure that forecasts are actionable and accessible for farmers, governments, and humanitarian actors.

The role emphasizes bridging science and practice by developing innovative, data-driven solutions that improve preparedness, reduce agricultural losses, and enhance climate resilience of vulnerable farmers under increasing climate risks. The work under the Specialist should also be done under CSI Agro-informatics guidance, following the One FAO Early Warning and Risk Monitoring approach.

• Technical agromet and early warning support — Translate climate and weather forecasts into agricultural risk insights for crops, livestock, and fisheries.
• Develop agro-advisories and decision-support tools for farmers and policy makers.
• Design and strengthen early warning systems integrating climate, hydrological, and agricultural data.
• Define risk thresholds and trigger mechanisms for anticipatory action, and support strengthening of their monitoring systems, including automation of these.
• Ensure last-mile communication of warnings through user-friendly channels and formats.
• Train extension officers and communities on interpreting early warnings and applying agro-advisories.
• Contribute to technical reports on best practices, approaches and policies to increase the development of advanced climate services for agriculture.
• Design and implement AI/ML tools to improve agromet and early warning systems — Apply AI/ML models for predictive analytics and strengthen early warning systems in data-poor areas.
• Integrate multiple data sources (remote sensing, IoT, farmer registries, climate models) into ML pipelines.
• Test and validate models for accuracy, scalability, and…