UNICEF's Data and Analytics team for Maternal, Newborn, Child, and Adolescent Health (MNCAH) needs to strengthen global data infrastructure, analytics tools, and documentation of best practices. They also need to provide technical support to country offices and ministries of health to access, clean, and analyze routine data, and finalize and scale country-specific data pipelines, dashboards, and analytics products. The core of this consultancy is to develop a technology suite that makes R script outputs accessible to large language models (LLMs) for natural language querying, thereby improving data accessibility and utility for decision-making across Africa.
Requirements
- Designing and maintaining reproducible data pipelines and automated reporting tools using R.
- Handling large, complex datasets, particularly routine health information system (RHIS) data (e.g., DHIS2), administrative datasets, or survey data (e.g., DHS, MICS).
- Developing statistical models and conducting data quality assessments (e.g., imputation, outlier detection, validation).
- Building interactive dashboards and data visualizations using R or Python packages.
- Supporting version control workflows using GitHub, GitLab, or other collaborative platforms.
- Experience working with cloud-based database systems (e.g., Azure Postgres, AWS RDS) and building ETL pipelines that integrate geospatial and facility-level data.
- Familiarity with global health indicator frameworks, especially those related to maternal health, newborn health, child health, primary health care, and health systems performance.
Responsibilities
- Design and maintain automated, scalable data pipelines to ingest, transform, and standardize data from diverse sources, including DHIS2, MICS, WorldPop, and administrative datasets.
- Operationalize core analytics functions, including coverage estimation, indicator computation, geospatial integration, and data quality checks.
- Build and modularize pipeline components to ensure reusability across countries and alignment with rapid-cycle analytics needs.
- Develop tailored outputs—such as dashboards, slide decks, and summaries—for use in country reviews, planning meetings, and supervision visits.
- Support maintenance and expansion of the MNCAH Global Database and related data infrastructure, including automation and optimization of regular data refreshes.
- Develop a technology suite that takes outputs and figures from R scripts and makes them accessible to large language models (LLMs) for natural language querying.
- Contribute to the integration of large language models (LLMs) and other AI tools into the AHEAD analytics package to streamline slide generation, narrative development, and structured summaries.
Other
- A bachelor’s degree in Data Science, Statistics, Computer Science, Epidemiology, Public Health, Engineering, Health Informatics, or a related quantitative field.
- A minimum of 5 years of progressively responsible professional experience in data science, data engineering, or health data analytics especially MNCAH data, with proven experience applying advanced data techniques to public health or development contexts.
- Experience supporting decision-makers in the use of data for decision-making, particularly in low- and middle-income countries.
- Excellent written and oral communication of technical content in English.
- Proven ability to work in a multicultural, interdisciplinary team and coordinate across HQ, regional, and country levels.
