Machine Learning Engineering Consultant, Data and Analytics, DAPM, NYHQ, remote. Req# 585424

About UNICEF

If you are a committed, creative professional and are passionate about making a lasting difference for children, the world's leading children's rights organization would like to hear from you. For 70 years, UNICEF has been working on the ground in 190 countries and territories to promote children's survival, protection and development. The world's largest provider of vaccines fordeveloping countries, UNICEF supports child health and nutrition, good water and sanitation, quality basic education for all boys and girls, and the protection of children from violence, exploitation, and AIDS. UNICEF is funded entirely by the voluntary contributions of individuals, businesses, foundations and governments. UNICEF has over 12,000 staff in more than 145 countries.

Consultancy: Machine Learning Engineering Consultant     

Duty Station: Data and Analytics, DAPM  

Duration: 1 December 2025 – 30 November 2026   

Home/ Office Based: Remote

 

BACKGROUND

Purpose of Activity/ Assignment:

The consultant will work in the Data and Analytics Section of the Office of Strategy and Evidence to support projects and activities related to machine learning (ML), artificial intelligence (AI), data analysis, and information extraction.

 

In particular, the consultant will:

• Maintain and optimize the vaccine stockout machine learning model already trained for UNICEF’s Program Group Immunization Division, ensuring its accuracy, performance, and sustainability.
• Continue to enhance methods for large-scale data and information extraction from diverse and unstructured document sources for the West Central Africa Region Social Policy teams and the WASH Analytics team before moving to additional domains.
• Support the development of automated briefs and reports generation pipelines.
• Test, evaluate, and implement robust frameworks for (semi)-automated GenAI report and data quality assurance.
• Contribute to geospatial (GIS) and AI-related initiatives, particularly as part of the Frontier Data Network Ahead of the Storm project.
• Provide technical advice on AI/ML approaches.
• Build reproducible workflows and contribute to machine learning and GenAI knowledge transfer within the team

Scope of Work:

• Maintain, retrain, and document existing ML models in production when new data or new features are available.
• AI assisted data and information extraction from unstructured documents.
• Prototypes and production-ready solutions for automated reporting.
• Contributions to GIS and AI project outputs.
• Define automated, AI driven data tests, also over GenAI outputs.

Terms of Reference / Key Deliverables:

Work Assignment Overview/Deliverables and Outputs/Delivery deadline

1.  Support Data and Analytics Section development of GenAI Retrieval Augmented Generation (RAG) driven SDG Country Briefs

-  RAG system that generates SDG country briefs that are contextualized per country with actionable information that helps countries understand where they are and are not meeting their SDG targets

31 December 2025

2.  Support UNICEF teams in extracting budget lines from governments’ public budget documents (particularly child- related budget items) data from unstructured documents for West Central Africa extract. The tool must be designed to be adaptable for extracting information in any domain

-  A generic data extraction pipeline using the most appropriate technologies (e.g., Data Bricks, Microsoft Document Intelligence and custom machine learning models) that retrieve and structure relevant data and  information from unstructured budget documents so that non-technical users can query and retrieve the structured data they require to analyse and track (including over time, through time series) national spending on child-related budget items

28 February 2026

3.  Develop and deploy a user-friendly interface for non-technical programme teams to work with the document extraction tool for extracting information on child-related budget items.

-  Data Bricks/Microsoft Document Intelligence data extraction pipeline equipped with a user-friendly interface to extract information on child-related expenditures from unstructured national budget documents, including dynamic links to sources of every number, LLM-generated analysis, time series, and charts.

31 March 2026

4.  Coordinate with stakeholders (PG-I, DAPM) to support maintenance of the released vaccine stockout model. Retrain as new data sources become available and assist in deployment as required

-  Vaccine stockout model maintained and integra