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An international organization in agriculture is seeking consultants for data migration and statistical analysis. Candidates must have an advanced degree in relevant fields and at least five years of experience in data management. Responsibilities include data cleaning, validation, and automation of data systems. A solid knowledge of statistical tools and quality assurance is essential. Working knowledge of English is required.
The Statistics Division (ESS) develops and advocates for the implementation of methodologies and standards for data collection, validation, processing and analysis of food and agriculture statistics. In these statistical domains, it also plays a vital role in the compilation, processing and dissemination of internationally comparable data, and provides essential capacity building support to member countries. In addition, the Division disseminates many publications, working papers and statistical yearbooks, which cover statistics relevant to agricultural and food security (including prices, production, trade and agri-environmental statistical data). The Statistics Division is involved in the management of a number of large-scale projects, aimed at improving statistical methodologies and establishing best practices for the collection, collation, processing, dissemination and use of data relevant to food security, agriculture and rural areas.
ESS is also mandated to strengthen the coordination of FAO statistical activities, to monitor the implementation of statistical standards across the statistical units and to provide quality assurance to all FAO statistical processes and statistical outputs. Within this context, the SDW project on the modernization and integration of the FAO statistical system is funded by Capital expenditures Fund (CAPEX) and aims to achieve the (i) implementation of an integrated Statistical Data Warehouse (SDW) and (ii) build a harmonized dissemination platform for FAO statistics.
Consultants and PSA subscribers in ESS will work under the general supervision of the Chief Statistician/Director of the Statistics Division and under the direct supervision of the Dissemination and Outreach Team Leader. They may be called upon to collaborate with other FAO Divisions and teams.
Consultants and PSA subscribers will support the data migration from FAO databases and systems to the Statistical Data Warehouse for dissemination in the external platform. They will use technical and analytical skills to analyse data and communicate their findings.
Consultants and PSA subscribers will contribute to and/or take responsibility for one or more of the following tasks:
•For Consultants: Advanced university degree from an institution recognized by the International Association of Universities (IAU)/UNESCO in Statistics, Mathematics, Data Sciences, Computer Sciences, Data Management or related disciplines; Consultants with bachelor\'s degree need two additional years of relevant professional experience.
•For PSAs: University degree from an institution recognized by the International Association of Universities (IAU)/UNESCO in Statistics, Mathematics, Data Sciences, Computer Sciences, Data Management or related disciplines.
•At least five years of years of relevant experience in collecting, manipulating, analysing and disseminating significant amounts of statistical data.
•Working knowledge (level C) of English.
•Results Focus
•Teamwork
•Communication
•Building Effective Relationships
•Knowledge Sharing and Continuous Improvement
•Extent of knowledge and relevant experience in statistical data management and data dissemination.
•Extent of knowledge of and relevant experience in the use of at least one of the data analysis tools such as R, Python, SQL, and Excel.
•Extent of knowledge of and relevant experience in relational databases, database design and database systems such as MySQL, PostgreSQL or MS SQL is desirable.
•Knowledge of the Statistical Data and Metadata eXchange (SDMX) standard and compliant tools will be an asset (such as dotStatSuite, FMR, FMR workbench).
•Ability to work independently, with minimum supervision.