Technical Specialist in Methods for Economic Modelling

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:

Machine-learning and predictive analytics:

•     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 model performance to improve analytical outcomes.

Data management and quantitative modelling:

•     Support the acquisition, preparation, integration, and quality assurance of large and diverse datasets.
•     Utilize programming languages and analytical software to perform supervised data analysis and model development.
•     Conduct basic data management to ensure reproducibility, transparency, and robustness of analytical workflows and modelling frameworks.

Macroeconomic and food security modelling:

•     Contribute through supervised tasks to the development and application of global macroeconomic and agrifood system simulation models.
•     Support through supervised tasks the assessment of future food insecurity, undernourishment, poverty, and resilience outcomes under alternative scenarios.
•     Assist the analyse of uncertainty and model sensitivities to strengthen evidence-based policy recommendations.
•     Assist the generation of quantitative evidence to support strategic planning and policy analysis.

Risk, uncertainty, and resilience analytics:

•     Apply analytical methods as directed to assess risks and uncertainties affecting agrifood systems.
•     Assist the development of quantitative approaches to evaluate the impacts of climate, economic, and policy shocks on food security outcomes.
•     Support the design of indicators and analytical frameworks for resilience assessment and monitoring.
•     Contribute through supervised tasks to methodological innovations that improve risk analysis and decision-making under uncertainty.

Communication, stakeholder engagement and knowledge dissemination:

•     Assist the preparation of communications of quantitative, statistical, and machine learning concepts to technical and non-technical audiences through reports, presentations, and policy briefs.
•     Assist in the presentation of analytical findings and methodological innovations to FAO colleagues, interdisciplinary technical teams, and senior management to support evidence-based decision-making.
•     Assist senior staff in the preparation and delivery of technical workshops, training sessions, and capacity-development activities on data analytics, modelling, and food security assessment.
•     Support the preparation of technical documentation, guidance materials, and knowledge products to facilitate the uptake and replication of analytical methods and tools.

CANDIDATES WILL BE ASSESSED AGAINST THE FOLLOWING

Minimum Requirements

•     Advanced university degree from an institution recognized by the International Association of Universities (IAU)/UNESCO in economics, mathematics, physics, computer sciences or statistics. Master’s level or above. Consultants with a bachelor's degree need two additional years of relevant professional experience.
•     At least 1 year of relevant experience in quantitative analysis using mathematical or computer science methods including applying models to sustainable agrifood systems or food insecurity.
•     Working knowledge (level C) of English.

FAO Core Competencies

•     Results Focus
•     Teamwork
•     Communication
•     Building Effective Relationships
•     Knowledge Sharing and Continuous Improvement

Technical/Functional Skills

•     Extent and relevance of experience in mathematical methods in manifold learning or machine learning, including experience in preparation of papers and/or reports for publication.
•     Extent and relevance of experience in analysis of issues in agrifood systems and food security at a national, regional and/or global scale.
•     Extent and relevant experience and knowledge of the main data sources for analysing agrifood systems, and of data compilation, validation, visualisation, and analysis.
•     Experience in temporal and spatial input data collection, including the analysis of correlation.
•     Proficiency in using programming and statistical software, especially R, Python or similar software.
•     Quality of both oral and written communication in English, including the ability to write clearly and concisely for publications.
•     Demonstrated ability to manage, analyse, and present quantitative information clearly and effectively.
•     Capacity to work effectively in multidisciplinary teams with minimal supervision and to plan workflows so as to meet tight deadlines.