Statistician - Roma, Lazio, Italy

Organizational context: The Statistics Division (ESS) at FAO headquarters in Rome, Italy, develops and advocates for methodologies and standards in food and agriculture statistics. It plays a key role in compiling, processing, and disseminating internationally comparable data, while also providing capacity building for member countries. The Division publishes reports and yearbooks covering agriculture and food security statistics. As FAO's Chief Statistician, the ESS Director ensures strong data governance, statistical excellence, and FAO's global presence in data discussions.

Job purpose: The Statistician will provide expertise to improve and develop methods for generating food security and nutrition statistics. This includes compiling, processing, validating, analyzing, storing, and disseminating data, with a focus on experience-based food security scales. The role involves ensuring correct application of methods for estimating food insecurity prevalence (FIES, PoU, PUA), producing high-quality statistical documents and tools, and collaborating with national and international partners on data collection, analysis, and reporting using FAO-endorsed methods.

Responsibilities:
Maintain and refine methodologies and statistical tools for computing food insecurity prevalence using FIES and other indicators. Develop analytic models and scoring strategies for FIES to ensure cross-country comparability. Analyze results to identify inconsistencies in indicators and propose solutions. Extend analytic codes and statistical methods for FIES data analysis using Item Response Theory. Review sampling frames and adjust weights for survey quality. Provide methodological guidance for other food security and nutrition indicators, including PoU and PUA. Refine methodologies for data disaggregation and variance computation. Conduct analyses of national datasets to inform global assessments. Contribute to publications on FIES methodology and empirical applications. Conduct research on extending methods to new applications. Disseminate and interpret FIES data analysis results across various platforms. Draft sections of the SOFI report concerning statistical data analysis and interpretation. Support computation of regional aggregates for FAOSTAT and other data platforms. Provide technical assistance and capacity development on FIES-based and other food security indicators to internal FAO teams, international agencies, NGOs, and academicians. Assist national statistical offices with analyses of food security data. Oversee development of web-based learning materials for software and model applications.

Education:
Advanced university degree (Master's or equivalent) in statistics, mathematics, economics, agricultural economics, or a related field. Requires five years of relevant experience in compiling and analyzing food and agricultural statistics, with a focus on methodological development and production of food security statistics in national or international organizations. Working knowledge of English (Level C) and intermediate knowledge of another FAO official language (Arabic, Chinese, French, Russian, or Spanish) is necessary.

Work

experience:
Five years of relevant experience in the compilation and analysis of food and agricultural statistics within national or international organizations. Experience should focus on the methodological development and production of food security statistics. Work experience in more than one location or area of work, particularly in field positions, is beneficial.

Skills:
Strong command of statistical methods including data presentation, analysis, descriptive statistics, probability distributions, correlation, sampling techniques, inference, hypothesis testing, linear regression, ANOVA, Chi-square tests, and nonlinear models. Familiarity with survey and questionnaire design. Extensive experience and knowledge of data sources for food security statistics compilation. Proven experience in compiling, validating, and analyzing food security statistics. Proficiency in data processing and analysis software such as R, STATA, SPSS, or SAS, along with strong statistical programming skills. In-depth knowledge of statistical methods for data analysis using Item Response Theory. Ability to analyze complex statistical issues.

Required languages: Arabic, Chinese, English, French, Russian, Spanish

Desired languages: