Technical Specialist in Advanced Methods for Economic Modelling

The Technical Specialist will specialize in data analysis using advanced mathematical or machine learning methods to contribute to reducing multi-dimensional agrifood system indicators and support flagship FAO initiatives such as SOFA and SOFI. The role involves developing innovative approaches for dimensional reduction, prediction, sensitivity analysis, and macroeconomic food security modelling to improve assessment, monitoring, and forecasting of food insecurity and sustainable agrifood system outcomes.

Responsibilities

• Develop and apply 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.
• Apply machine learning methods to uncertainty analysis, sensitivity assessment, and scenario evaluation across agrifood system projects.
• Enhance data processing, feature engineering, and model performance to improve analytical outcomes.
• Support the acquisition, preparation, integration, and quality assurance of large and diverse datasets.
• Utilize programming languages and analytical software to perform advanced data analysis and model development.
• Ensure reproducibility, transparency, and robustness of analytical workflows and modelling frameworks.
• Contribute to the development and application of global macroeconomic and agrifood system simulation models.
• Support the assessment of future food insecurity, undernourishment, poverty, and resilience outcomes under alternative scenarios.
• Analyse uncertainty and model sensitivities to strengthen evidence-based policy recommendations.
• Generate quantitative evidence to support strategic planning and policy analysis.
• Apply advanced analytical methods to assess risks and uncertainties affecting agrifood systems.
• Develop 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 to methodological innovations that improve risk analysis and decision-making under uncertainty.
• Communicate complex quantitative, statistical, and machine learning concepts to technical and non-technical audiences through reports, presentations, and policy briefs.
• Present analytical findings and methodological innovations to FAO colleagues, interdisciplinary technical teams, and senior management to support evidence-based decision-making.
• Engage with FAO research partners, and external stakeholders as required to explain analytical methods, modelling results, and their policy implications.
• Contribute to the preparation and delivery of technical workshops, training sessions, and capacity-development activities on data analytics, modelling, and food security assessment.
• Support senior colleagues in the development of strategic analytical products, flagship publications, and technical advisory services.
• Prepare technical documentation, guidance materials, and knowledge products to facilitate the uptake and replication of analytical methods and tools.
• Represent the team in internal and external meetings, conferences, and working groups, contributing technical expertise on food security, agrifood systems, and advanced analytical methods.

Requirements

• Advanced university degree from an institution recognized by the International Association of Universities (IAU)/UNESCO in economics, mathematics, physics, computer sciences or statistics. Consultants with a bachelor's degree need two additional years of relevant professional experience.
• At least 5 years of relevant experience in quantitative analysis of agrifood systems, including applying advanced models to sustainable agrifood systems or food insecurity.
• Working knowledge (level C) of English.
• Experience in mathematical methods in manifold learning or machine learning, including preparation of papers and/or reports for publication.
• Experience in analysis of issues in agrifood systems and food security at a national, regional and/or global scale.
• 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.

Skills

• Quantitative Analysis
• Agrifood Systems Analysis
• Sustainable Agrifood Modelling
• Food Security Analysis
• Mathematical Methods
• Manifold Learning
• Machine Learning
• Data Compilation
• Data Validation
• Data Visualization
• Temporal Data Analysis
• Spatial Data Analysis
• Correlation Analysis
• R Programming
• Python Programming
• Statistical Software Stata
• Quantitative Information Presentation
• Scientific Report Writing
• Dimensionality Reduction
• Prediction Modelling
• Sensitivity Analysis
• Macroeconomic Food Security Modelling

Languages

English