Technical Specialist in Methods for Economic Modelling
Organizational Context
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. ESA provides evidence-based support for national, regional, and global policy processes concerning food and agricultural policies, agribusiness, rural transformation, poverty, food security, nutrition, resilience, bioeconomy, and climate-smart agriculture. The division also leads the production of key FAO publications like The State of Food and Agriculture (SOFA) and The State of Food Security and Nutrition in the World (SOFI).
Job Purpose
The Technical Specialist will focus on data analysis using mathematical or machine learning methods, specifically supporting efforts in dimensional reduction for agrifood system indicators. This includes tracking progress toward sustainable agrifood systems, consolidating features for machine learning in food insecurity analysis, and assessing undernourishment and poverty. The role contributes innovative analysis to flagship reports (SOFA, SOFI) and FAO's food insecurity monitoring platforms. The specialist will assist in developing and applying innovative approaches for dimensional reduction, prediction, sensitivity analysis, and macroeconomic food security modelling, thereby improving the assessment, monitoring, and forecasting of food insecurity, undernourishment, poverty, and sustainable agrifood system outcomes.
Responsibilities
The incumbent will support machine-learning and predictive analytics by assisting in the development and application of models for prediction, classification, inference, and decision support, contributing to food insecurity forecasting, risk monitoring, and early warning systems. They will apply machine learning methods to uncertainty analysis, sensitivity assessment, and scenario evaluation, and enhance data processing and model performance. Responsibilities also include supporting data acquisition, preparation, integration, and quality assurance, utilizing programming languages for data analysis and model development, and conducting basic data management for reproducibility. Furthermore, the role involves contributing to the development and application of global macroeconomic and agrifood system simulation models, assessing future food insecurity, undernourishment, poverty, and resilience outcomes, and analyzing uncertainty and model sensitivities. The specialist will apply analytical methods to assess risks and uncertainties affecting agrifood systems, assist in developing quantitative approaches to evaluate impacts of shocks, and support the design of indicators for resilience assessment. Communication tasks include preparing and presenting quantitative findings to diverse audiences, assisting in workshops and training, and preparing technical documentation.
Work Experience
A minimum of one year of relevant experience in quantitative analysis using mathematical or computer science methods is required. This experience should include applying models to sustainable agrifood systems or food insecurity. Proficiency in English at a working level (level C) is also necessary.
Skills
Key skills include proficiency in mathematical methods for manifold learning or machine learning, with experience in preparing publications. Experience in analyzing agrifood systems and food security issues at national, regional, or global scales is important, along with knowledge of relevant data sources and data compilation, validation, visualization, and analysis techniques. Experience with temporal and spatial data collection and correlation analysis is beneficial. Proficiency in programming and statistical software such as R or Python is required. Strong oral and written communication skills in English, the ability to manage, analyze, and present quantitative information effectively, and the capacity to work in multidisciplinary teams with minimal supervision to meet deadlines are essential.