Organizational Setting

FAO assists Armenia on climate change issues and in its engagement on the national NDC processes with a special focus in the agriculture sector, forestry and Land Use, Land-Use Change and Forestry (LULUCF). The agriculture sector as well as forestry (afforestation, forest protection) and LULUCF (other land uses) are considered among the key sectors in the country´s revised NDC. Therefore, in line with the global mandate of FAO, support will be provided to integrate and ensure participation of the agriculture, forestry and LULUCF stakeholders in the National activities related to NDC update, implementation, as well as other climate change related initiatives in the country.

Reporting Lines

The Data Collection and Analysis Specialist will work under the overall supervision of the FAO Representative in Armenia (REU and FAO Senior Policy Officer (REU) and in close collaboration with FAO National Expert on Socio-Economic Analysis.

Technical Focus 

The Data Collection and Analysis Specialist will collect the stock of datasets containing socio-economic data related to the agricultural sector.

Tasks and responsibilities

•    Collect the stock of datasets containing socio-economic data related to the agricultural sector.
•    Analyse the data and identify key parameters correlating with agricultural income.
•    Contribute to the development of methodology for agricultural income calculation.
•    Perform other duties as required.

CANDIDATES WILL BE ASSESSED AGAINST THE FOLLOWING

Minimum Requirements

•    Advanced University degree in Statistics, Agricultural Economics, Rural Development, Social Sciences or a closely related technical field.
•    At least 5 years of relevant work experience in statistical analysis applied to agriculture and socio-economic studies.
•    Working knowledge of English and Armenian languages
•    National of Armenia or resident in the country with a valid work permit.

FAO Core Competencies

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

Selection Criteria

•    Working knowledge of advanced statistics with ability to use statistical software packages.