FAO Technical Specialist in Methods for Economic Modelling

Job Posting Organization:
The Agrifood Economics and Policy Division (ESA) of the FAO is dedicated to conducting economic research and policy analysis aimed at transforming agrifood systems into more efficient, inclusive, resilient, and sustainable frameworks. Established to support better production, nutrition, environment, and overall quality of life, ESA operates globally, providing evidence-based support to various policy processes and initiatives. The division is responsible for flagship publications such as The State of Food and Agriculture (SOFA) and The State of security" style="border-bottom: 1px dotted #007bff !important;">security" style="border-bottom: 1px dotted #007bff !important;">Food Security and Nutrition in the World (SOFI), and it plays a crucial role in the FAO Global Roadmap.

Job Overview:
The Technical Specialist position focuses on data analysis utilizing mathematical and machine learning methods. The role involves supporting efforts to reduce multi-dimensional agrifood system indicators into lower-dimensional representations, which is essential for tracking national progress towards sustainable agrifood systems. The incumbent will contribute to significant reports and initiatives, enhancing the assessment and forecasting of food insecurity, undernourishment, and poverty. The position requires innovative approaches to dimensional reduction, prediction, sensitivity analysis, and macroeconomic food security modeling, thereby supporting flagship FAO initiatives and reports.

Duties and Responsibilities:
The incumbent will be responsible for a variety of tasks, including:

• Machine-learning and predictive analytics: Assisting in the development and application of machine learning models for prediction and classification, contributing to food insecurity forecasting, and enhancing data processing and model performance.
• Data management and quantitative modeling: Supporting the acquisition and quality assurance of diverse datasets, utilizing programming languages for data analysis, and ensuring reproducibility and transparency in analytical workflows.
• Macroeconomic and food security modeling: Contributing to the development of global simulation models, assessing future food insecurity and resilience outcomes, and generating quantitative evidence for policy recommendations.
• Risk, uncertainty, and resilience analytics: Applying analytical methods to assess risks affecting agrifood systems and developing quantitative approaches to evaluate impacts of various shocks on food security.
• Communication and stakeholder engagement: Preparing communications for technical and non-technical audiences, presenting findings to FAO colleagues, and assisting in workshops and training sessions.

Required Qualifications:
Candidates must possess an advanced university degree in economics, mathematics, physics, computer sciences, or statistics from a recognized institution. A master's degree or higher is required, while those with a bachelor's degree must have two additional years of relevant professional experience. At least one year of relevant experience in quantitative analysis using mathematical or computer science methods is also necessary, particularly in relation to sustainable agrifood systems or food insecurity. A working knowledge of English at level C is mandatory.

Educational Background:
The educational background required for this position includes an advanced university degree from an institution recognized by the International Association of Universities (IAU)/UNESCO in relevant fields such as economics, mathematics, physics, computer sciences, or statistics. A master's level or above is preferred, and candidates with a bachelor's degree must have additional professional experience to qualify.

Experience:
The position requires at least one year of relevant experience in quantitative analysis, specifically using mathematical or computer science methods. This experience should include applying models to sustainable agrifood systems or food insecurity, demonstrating a solid understanding of the field.

Languages:
A working knowledge of English at level C is mandatory for this position. Proficiency in additional languages may be beneficial but is not specified as a requirement.

Additional Notes:
The job is remote, allowing for flexibility in work location. The position is likely to be full-time, and candidates may be assessed against FAO core competencies such as results focus, teamwork, communication, and building effective relationships. The role may involve collaboration with multidisciplinary teams and requires the ability to manage and present quantitative information effectively.