Organizational Setting

FAO Statistics Division (ESS) has the mandate to: 
•     Collect, analyze and disseminate agrifood systems data from member countries, generating statistics and accompanying briefs in FAOSTAT, RuLIS and other relevant platforms.
•     Work directly with countries to develop national statistical strategies, strengthen institutional and technical capacities and improve statistical systems.
•     Develop and promote international food and agricultural statistical standards, as well as methods and tools for collecting, analyzing and disseminating data.
•     Ensure stronger governance of FAO's statistical system.
•     Foster consistency of FAO's Statistical programme with the Strategic Framework.
•     Ensure statistical excellence through the implementation of statistical standards and quality assurance mechanisms.
•     Position FAO's presence in statistical discussions at global level.

Within this mandate, ESS is collaborating with technical FAO divisions and decentralized offices, to develop and promote Earth observation (EO)-based solutions to produce data and statistics needed for evidence-based decision-making in support of food security and food system transformation. ESS and its partners also provide technical assistance to countries on the use of EO data in the production and quality assurance of agricultural statistics, including through the implementation of initiatives such as the FAO-EOSTAT, the 50x2030 Initiative and the World Programme for the Census of Agriculture (WCA). Finally, ESS, through its Data Lab for Statistical Innovation, supports FAO divisions and offices in the design and experimentation of innovative solutions using new methods and data sources, including those related to geospatial information, to produce actionable information (e.g. for climate resilience, early warning systems and disaster impact assessments) and statistics for the implementation of FAO Strategic Framework.

Reporting Lines

The selected candidate will work under the supervision of the Team leader of the Methodological Innovation team in the Statistics Division (ESS), in close collaboration with the Agrifood Economics and Policy Division (ESA), FAO regional and country offices and other relevant FAO divisions/offices on the development, promotion and capacity development activities of EO-based solutions for food and agriculture statistics and evidence-based decision-making, including through the activities of the EOSTAT, 50x2030 Initiative, WCA and Data Lab for statistical innovation.

Technical Focus

The incumbent will provide technical support to ESS-led geospatial activities related to the use of Earth Observation (EO) data for the production and quality assurance of agricultural statistics. The focus will be on providing expertise in applying EO data to address the practical realities and data challenges of agricultural monitoring and statistical production in developing countries, in close collaboration with ESS and relevant technical partners.
The incumbent will have responsibilities in the following areas of work: Development and application of EO based methodologies for agricultural statistics; Integrated data systems, interoperability and quality assurance; Validation and quality assessment of EO derived products; Collaboration with countries and partners; Capacity development, outreach and knowledge transfer; Communication, dissemination and policy relevance.

Tasks and responsibilities

•     Design, implement and validate EO based methodologies and indicators for crop area and yield, climate resilience, early warning systems and disaster impact assessments, to enhance existing national agricultural statistical frameworks while ensuring scalability and fitness for purpose in developing country contexts (small holder farming systems, mixed cropping, common land tenure).

•     Support the preprocessing and use of EO satellite images and time series, and the preparation and use of EO datasets for the EOSTAT, the 50x2030 Initiative and the WCA activities as needed.
•     Apply geospatial data science and machine learning approaches (e.g. for crop type and land cover mapping, improved area frames and farm registers, for scalable statistical indicators derived from various sources (survey data, national statistics, geospatial data including EO time series and other auxiliary information)) with the objective of filling statistical data gaps and improve statistical processes.
•     Design and support integrated data collection strategies combining EO, census, survey and in situ data, including data collection and sampling protocols for in situ data and their integration in agricultural surveys/censuses, and the design of digital data collection systems (e.g. ODK, SurveyCTO) to generate essential ground-truth data for model calibration, validation and complementary socio-economic analysis.
•     In line with EOSTATs framework for in situ data quality assurance, establish end-to-end data quality assurance protocol, from real-time monitoring during field work to post-collection cleaning, harmonization, and documentation of integrated datasets using tools like R, SPSS and QGIS.
•     Foster data interoperability by linking census/survey and statistical datasets with geospatial data layers (e.g. climate, soil, satellite-derived products) and aligning them with national and global data frameworks.
•     Collaborate with and/or provide technical assistance to national technical counterparts (statisticians, agronomists, policy analysts) to integrate EO-derived evidence into official statistical production cycles, national crop forecasts, early warning bulletins and disaster impact assessments. 
•     Contribute to the expansion of the EOSTAT and the Data Lab for statistical innovation initiatives, notably by establishing effective collaboration with partners, contributing to the scoping of activities and priorities, and assessing data availability, needs, gaps and opportunities. 
•     Contribute to capacity development activities tailored to the operational needs of national agricultural and statistical agencies, notably by preparing adapted training material, technical documentation, notes, and inputs to training or knowledge-sharing activities and providing tailored training and technical assistance to national counterparts.
•     Support outreach activities to foster use of EO data access services and platforms in developing countries (e.g. cloud-based EO services and APIs) in line with ESS guidance.
•     Translate geospatial analysis and trends into clear, actionable technical notes, policy briefs and programme recommendations addressing agriculture production, productivity, and climate adaptation needs.
•     Support the dissemination of geospatial products produced by the Statistics, including the preparation and maintenance of relevant ISO compliant metadata in FAO Agro informatics platforms.

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 Environmental Science, Data Science, Informatics, Geography, Statistics or related fields; Consultants with a bachelor's degree need two additional years of relevant professional experience; 
•     Minimum of 5 years of relevant experience in applying EO Data to agricultural monitoring, food and agriculture statistics, or evidence-based policy analysis with a demonstrable focus on developing country contexts;
•     Working knowledge of English.  

FAO Core Competencies

•     Results Focus
•     Teamwork
•     Communication
•     Building Effective Relationships
•     Knowledge Sharing and Continuous Improvement

Selection Criteria

•     Extent and relevance of work experience in:

o    Applying EO data to address real-world agricultural and data challenges in developing countries (e.g., for national crop area estimation, yield monitoring and forecasting, drought monitoring, disaster assessment and development of area frames);
o    Conducting satellite image classification in heterogeneous agricultural landscapes typical of developing countries;
o    Designing and managing complex agricultural data collection (e.g. the development of data collection and sampling protocols for in-situ data, their integration in agricultural surveys, and the design of digital data collection systems (e.g. ODK, SurveyCTO);
o    Pre processing and analysing satellite image time series for agricultural monitoring and statistical applications;
o    Performing in situ data cleaning, processing and analysis in combination with other datasets (survey, statistical and geospatial data) using appropriate statistical and spatial tools (e.g. R, SPSS, QGIS);
o    Translating geospatial insights into clear policy advice, programme recommendations, or technical reports tailored for national government and development partners;
o    Designing, validating and applying operational, context-appropriate EO-derived indicators for crop performance, productivity and climate impacts, taking into account local agronomic practices;
o    Managing data integration, interoperability and quality assurance processes for geospatial applications;
o    Supporting capacity development and institutional strengthening, including the preparation of training materials, facilitation of, and providing technical support to national institutions
o    Working with agricultural statistics and geospatial data produced by national agencies in developing countries;

•     In addition, candidates will be assessed on:

o    Demonstrated understanding of the structure, constraints and opportunities of agricultural statistics systems and farming systems in developing countries;
o    Proven ability to collaborate effectively with both technical and non-technical national counterparts to integrate EO-based evidence into existing national monitoring, reporting and statistical production workflows;
o    Proficiency in the use of spatial analysis tools (notably R and QGIS), including experience in addressing common data quality issues in developing regions.
o    Ability to work independently, with minimum supervision.

•     The following are considered as assets:

o    Knowledge of Sen4STAT methodology and tools;
o    Knowledge of Spanish and French