Overview
Provide technical expertise to support the production of Common Operational Datasets on Population Statistics, related data products, analyses, documentation, and dissemination tools to strengthen humanitarian preparedness and response programming.
Key Responsibilities
- Support the generation and refinement of Common Operational Data Sets on Population Statistics.
- Provide analytic and technical support to UNFPA leadership of and engagement with the Inter-agency working group on interoperability of baseline population data, displacement data and refugee data.
- Contribute to the development of data quality assurance processes and estimation methodology.
- Contribute to the development of technical standards, data protocols and methods, and tools for the routine integration and harmonization of baseline COD-PS data.
- Contribute to updates of and dissemination of technical guidance briefs and technical tools.
- Provide technical support to UNFPA and UN Humanitarian partners to improve the precision and timeliness of vulnerable subpopulations of concern.
- Collaborate with UNFPA website focal points to provide and maintain data components.
- Respond to data related requests and questions from internal and external partners.
- Actively support the provision of Humanitarian Data expertise across the Fund.
- Support UNFPA Country Offices in engaging Information Management Working Groups of UN Humanitarian Country Teams.
- Contribute to the design, implementation and evaluation of population data analysis, capacity development workshops, webinars, and related activities.
- Provide data analytic support to UNFPA Country Offices and Regional Offices.
- Generate analysis of data from household surveys and population censuses.
- Contribute to UNFPA’s collaborations with OCHA, IOM, and UNHCR.
- Contribute to the generation of Common Operational Datasets on Population Statistics.
- Contribute to innovative data dissemination tools including Population Data Dashboards.
- Develop approaches for estimating the number of pregnant women in humanitarian settings.
- Contribute to technical alignment of UNFPA subnational population estimates and projection.
- Contribute to UNFPA’s inputs and recommendations to the IASC Task Force on the use of artificial intelligence in humanitarian contexts.
- Perform other analytic tasks as needed.
- Participate in a range of UN Inter-Agency Working Groups on humanitarian data.
- Substantively contribute to technical support efforts for UNFPA’s mobilization of additional resources in the data area.
- Participate in interdivisional and partnership mechanisms.
- Participate in the development, maintenance and dissemination of a set of M&E tools.
- Contribute to routine technical and programmatic reporting on UNFPA’s work on population data and estimation.
- Carry out any other duties as may be required.
Required Experience
- Minimum of two (2) years of progressively responsible professional experience in population data analysis, demographic analysis, statistics, data management, or applied quantitative research in an international organization, humanitarian agency, academic institution, or development organization (with an advanced university degree).
- OR
- Minimum of four (4) years of progressively responsible professional experience in population data analysis, demographic analysis, statistics, data management, or applied quantitative research in an international organization, humanitarian agency, academic institution, or development organization (with a first-level university degree).
- Experience in data collection, analysis, visualization, dissemination, database management and research within an academic or development institution in an international setting.
- Experience in constructing subnational population projections in low- and middle-income countries.
- Familiarity and/or experience working with refugee and internal displacement data from international humanitarian settings is an asset.
- Ability and experience in meeting tight deadlines to support humanitarian response and disaster preparedness.
- Familiarity and experience with mark-up languages such as MarkDown, RMarkDown and dashboard development tools, such as R Shiny, is an asset.
- Familiarity and experience with version control systems (e.g. Git, GitHub, GitLab, subVersion) and data dissemination standards (e.g. Humanitarian Exchange Language (HXL), Statistical Data and Metadata Exchange (SDMX), etc.).
Qualifications
- Advanced university degree (Master's degree or equivalent) in Demography, Statistics, Epidemiology, Public Health, Sociology, Economics, Data Science, Population Studies, or another closely related quantitative social science discipline.
- OR
- First-level university degree (Bachelor's degree) in Demography, Statistics, Epidemiology, Public Health, Sociology, Economics, Data Science, Population Studies, or another closely related quantitative social science discipline.
