Technical Specialist in Advanced 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, food security, nutrition, resilience, bioeconomy, and climate-smart agriculture. The division 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 apply advanced mathematical or machine learning methods to data analysis, focusing on dimensional reduction of agrifood system indicators. This includes consolidating features for machine learning in food insecurity assessments, macroeconomic simulations, and evaluating undernourishment and poverty. The role contributes innovative analysis to FAO's flagship reports (SOFA, SOFI) and food insecurity risk monitoring platforms. The incumbent will analyze and model complex agrifood system data, developing novel approaches for dimensional reduction, prediction, sensitivity analysis, and macroeconomic food security modeling to enhance the assessment, monitoring, and forecasting of food insecurity, undernourishment, poverty, and sustainable agrifood system outcomes.
Responsibilities
Develop and apply machine learning models for prediction, classification, and decision support, contributing to food insecurity forecasting, risk monitoring, and early warning systems. Apply machine learning to uncertainty analysis, sensitivity assessment, and scenario evaluation. Enhance data processing, feature engineering, and model performance. Support the acquisition, preparation, integration, and quality assurance of large datasets, utilizing programming languages for advanced analysis and model development, ensuring reproducibility and transparency. Contribute to global macroeconomic and agrifood system simulation models, assessing future food insecurity, undernourishment, poverty, and resilience outcomes under various scenarios. Analyze uncertainty and model sensitivities to strengthen policy recommendations and generate quantitative evidence for strategic planning. Apply advanced methods to assess risks and uncertainties affecting agrifood systems, developing quantitative approaches to evaluate impacts of shocks and supporting resilience assessment frameworks. Communicate complex concepts to diverse audiences through reports and presentations, engage with stakeholders, and contribute to technical workshops and capacity development activities. Prepare technical documentation and knowledge products, and represent the team in meetings.
Work Experience
At least 5 years of relevant experience in quantitative analysis of agrifood systems, including the application of advanced models to sustainable agrifood systems or food insecurity.
Skills
Proficiency in mathematical methods for manifold learning or machine learning, with experience in preparing publications. Experience analyzing agrifood systems and food security issues at various scales. Knowledge of data sources for agrifood systems analysis, including data compilation, validation, visualization, and analysis. Experience in temporal and spatial data collection and correlation analysis. Proficiency in programming and statistical software (e.g., R, Python). Excellent oral and written communication in English, with the ability to write clearly and concisely. Demonstrated ability to manage, analyze, and present quantitative information effectively. Capacity to work effectively in multidisciplinary teams with minimal supervision and manage workflows to meet deadlines.