Technical Specialist in Methods for Economic Modelling, Various Locations

Job Description - Technical Specialist in Methods for Economic Modelling (2601887)

Job Description

CALL FOR EXPRESSIONS OF INTEREST - VACANCY ANNOUNCEMENT : 2601887

Technical Specialist in Methods for Economic Modelling

Job Posting: 04/Sep/2026

Closure Date: 18/Sep/2026, 9:59:00 PM

Organizational Unit : ESA - Agrifood Economics and Policy Division

Job Type: Non-staff opportunities

Type of Requisition : Consultant / PSA (Personal Services Agreement)

Primary Location: Various Locations

Duration : Up to 11 months (renewable)

Post Number : N/A

IMPORTANT NOTICE: Please note that Closure Date and Time displayed above are based on date and time settings of your personal device

• FAO is committed to achieving workforce diversity in terms of gender, nationality, background and culture.
• Qualified female applicants, qualified nationals of non-and under-represented Members and person with disabilities are encouraged to apply;
• Everyone who works for FAO is required to adhere to the highest standards of integrity and professional conduct, and to uphold FAO's values
• FAO, as a Specialized Agency of the United Nations, has a zero-tolerance policy for conduct that is incompatible with its status, objectives and mandate, including sexual exploitation and abuse, sexual harassment, abuse of authority and discrimination
• All selected candidates will undergo rigorous reference and background checks
• All applications will be treated with the strictest confidentiality

FAO’s commitment to environmental sustainability is integral to our strategic objectives and operations.

Organizational Setting

The Agrifood Economics and Policy Division (ESA) conducts economic research and policy analysis to support the transformation to more efficient, inclusive, resilient and sustainable agrifood systems for better production, better nutrition, a better environment, and a better life, leaving no one behind. ESA provides evidence-based support to national, regional and global policy processes and initiatives related to monitoring and analysing food and agricultural policies, agribusiness and value chain development, rural transformation and poverty, food security and nutrition information and analysis, resilience, bioeconomy, and climate-smart agriculture. The division also leads the production of two FAO flagship publications: The State of Food and Agriculture (SOFA) and The State of Food Security and Nutrition in the World (SOFI) and provides core technical support for the FAO Global Roadmap.

Reporting Lines

Selected candidates will be assigned to different workstreams of the division and to different supervisors. The overall supervision remains with the Director, ESA.

Technical Focus

The Technical Specialist will specialise in data analysis using mathematical or machine learning methods. On dimensional reduction the incumbent is expected to support efforts reducing multi-dimensional set of agrifood system indicators with non-constant substitutions and interactions to lower dimensional representations, with applications including tracking national progress toward sustainable agrifood system, consolidating input features for machine learning in food insecurity and uncertainty in macroeconomic simulation and assessment of future undernourishment and poverty. The incumbent’s work will contribute innovative analysis to flagship reports State of Food and Agriculture (SOFA) and The State of Food Security and Nutrition in the World (SOFI) and the FAO’s food insecurity risk monitoring and situation platforms. The Technical Specialist supports analyses and modelling agrifood system data, assisting development in ESA of innovative approaches for dimensional reduction, prediction, sensitivity analysis, and macroeconomic food security modelling. Their work supports flagship FAO initiatives and reports, improving the assessment, monitoring, and forecasting of food insecurity, undernourishment, poverty, and sustainable agrifood system outcomes.

Tasks and responsibilities

In particular, the incumbent will support the following tasks under guidance of senior staff:

• Assist development and application of machine learning models for prediction, classification, inference, and decision-support applications.
• Contribute to food insecurity forecasting, risk monitoring, and early warning systems through advanced predictive analytics.
• Assist application of machine learning methods to uncertainty analysis, sensitivity assessment, and scenario evaluation across agrifood system projects.
• Enhance through supervised tasks data processing, feature engineering, and