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- FAO is committed to achieving workforce diversity in terms of gender, nationality, background and culture.
- Qualified female applicants, qualified nationals of non-and under-represented Members and person with disabilities are encouraged to apply;
- Everyone who works for FAO is required to adhere to the highest standards of integrity and professional conduct, and to uphold FAO's values
- FAO, as a Specialized Agency of the United Nations, has a zero-tolerance policy for conduct that is incompatible with its status, objectives and mandate, including sexual exploitation and abuse, sexual harassment, abuse of authority and discrimination
- All selected candidates will undergo rigorous reference and background checks
- All applications will be treated with the strictest confidentiality
FAO’s commitment to environmental sustainability is integral to our strategic objectives and operations.
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 advanced mathematical or machine learning methods. On dimensional reduction the incumbent is expected to contribute to 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 analyses and models complex agrifood system data, developing 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 following tasks are expected to be conducted by the technical specialist:
Machine-learning and predictive analytics:
• 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.
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 advanced data analysis and model development.
• Ensure reproducibility, transparency, and robustness of analytical workflows and modelling frameworks.
Macroeconomic and food security modelling:
• 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.
Risk, uncertainty, and resilience analytics:
• 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.
Communication, stakeholder engagement and knowledge dissemination:
• 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.
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. 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.
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.
Please note that all candidates should adhere to FAO Values of Commitment to FAO, Respect for All and Integrity and Transparency
ADDITIONAL INFORMATION
- FAO does not charge any fee at any stage of the recruitment process (application, interview, processing)
- Please note that FAO will only consider academic credentials or degrees obtained from an educational institution recognized in the IAU/UNESCO list
- Please note that FAO only considers higher educational qualifications obtained from an institution accredited/recognized in the World Higher Education Database (WHED), a list updated by the International Association of Universities (IAU) / United Nations Educational, Scientific and Cultural Organization (UNESCO). The list can be accessed at http://www.whed.net/
- For more information, visit the FAO employment website
- Appointment will be subject to certification that the candidate is medically fit for appointment, accreditation, any residency or visa requirements, and security clearances.
HOW TO APPLY
• To apply, visit the recruitment website at Jobs at FAO and complete your online profile. We strongly recommend that your profile is accurate, complete and includes your employment records, academic qualifications, and language skills
• Candidates are requested to attach a letter of motivation to the online profile
• Once your profile is completed, please apply, and submit your application
• Please note that FAO only considers higher educational qualifications obtained from an institution accredited/recognized in the World Higher Education Database (WHED), a list updated by the International Association of Universities (IAU) / United Nations Educational, Scientific and Cultural Organization (UNESCO). The list can be accessed at http://www.whed.net/. These qualifications should be in alignment with the International Standard Classification of Education (ISCED) mappings.
• Candidates may be requested to provide performance assessments and authorization to conduct verification checks of past and present work, character, education, military and police records to ascertain any and all information which may be pertinent to the employment qualifications
• Incomplete applications will not be considered
• Personal information provided on your application may be shared within FAO and with other companies acting on FAO’s behalf to provide employment support services such as pre-screening of applications, assessment tests, background checks and other related services. You will be asked to provide your consent before submitting your application. You may withdraw consent at any time, by withdrawing your application, in such case FAO will no longer be able to consider your application
• Only applications received through the FAO recruitment portal will be considered
• Your application will be screened based on the information provided in your online profile
• We encourage applicants to submit the application well before the deadline date.
If you need help or have queries, please create a one-time registration with FAO’s client support team for further assistance: https://fao.service-now.com/csp
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