Data Analyst Specialist

Organizational Context

UNFPA is the lead UN agency dedicated to reproductive health, aiming to end preventable maternal deaths, unmet family planning needs, and gender-based violence. This assignment is part of the UNFPA-UNICEF Joint Programme on the Elimination of Female Genital Mutilation (JP FGM), the largest global initiative against FGM. Operating in its fourth phase (2022-2025) across 17 priority countries in Africa and the Middle East, the programme focuses on accelerating FGM abandonment by addressing social norms, health consequences, and structural drivers.

Job Purpose

This UNV assignment supports the UNFPA-UNICEF Joint Programme on the Elimination of Female Genital Mutilation (JP FGM), a critical global initiative. The Data Analyst Specialist will play a key role in strengthening results-based management by generating and analyzing data related to FGM incidence, prevalence, and attitudes across multiple countries. The role involves integrating various data sources, including health information systems, surveys, social media, and programmatic data, to provide comprehensive insights. The specialist will also pilot new data collection methodologies for social norm indicators and for reaching hidden populations, contributing to evidence-based strategies for FGM abandonment and the overall achievement of UNFPA's transformative results.

Responsibilities

The Data Analyst Specialist will strengthen results-based management by generating FGM risk and attitudinal incidence data from sources like DHIS and MICS for 18 countries. They will produce subnational administrative estimates of FGM prevalence and integrate the FGM social media database (2008-2025) into UNFPA’s DIOS platform. The role involves linking geospatial programmatic data with risk incidence, prevalence estimates, social media signals, and Google embeddings. Additionally, the specialist will pilot test social norm indicators within existing health systems using ODK or Kobo Toolbox and pilot data generation among small or hidden populations using respondent-driven sampling. Expected outputs include comprehensive data reports and recommendations from pilot exercises.

Work Experience

Requires 3 years of relevant experience. Candidates should possess a strong interdisciplinary background in data science with extensive experience in statistical analysis of large datasets. Expertise in geospatial and AI-driven analysis using tools like QGIS, Python, R, and large language models is preferred. Experience with large-scale data management systems (e.g., DHIS2, SQL) and designing indicator measurement using survey methodology and digital data collection tools (e.g., ODK, Kobo Toolbox) is also valuable.

Skills

Key competencies include accountability, adaptability, client orientation, commitment to learning, communication, creativity, integrity, planning, professionalism, and teamwork. Technical skills encompass statistical analysis, geospatial and AI-driven analysis, data management (DHIS2, SQL), and digital data collection tools. Expertise in development programmes, community development, and information technology is also relevant.

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