UNICEF is the world's leading children's rights organization, dedicated to promoting children's survival, protection, and development. The Machine Learning Engineering Consultant will work in the Data and Analytics Section to support projects related to machine learning and artificial intelligence, enhancing data analysis and information extraction methodologies.

Responsibilities

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.
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.
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
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
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.
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 integrated with the existing data pipeline and producing predictions consumable by Power BI and other systems for the current set of 30 countries
Automated AI driven data and RAG-generated report QA - Research phase?
Test and report results from multiple evaluation frameworks, including but not limited to DeepEval, LangChain Evaluation, TruLens, and RAGAS.
Automated AI driven data and RAG-generated report QA - Implementation phase?
Implement into the production lifecycle and document the evaluation framework(s) that deliver the best results for the SDG Country Briefs and Document Extraction products
Work with D&A Geospatial lead to integrate the use of satellite embedding datasets to drive new geospatial analytics in our enterprise geospatial and data platforms
Geospatial foundation models integrated into data and analytics workstreams, available through the Data and Analytics section Data Bricks environment and optionally Snowflake
Problem preparation and technical support of external engagement
Pitch (2-pager and presentation), data preparation and demo materials needed to engage with leading expert on a technical collaboration for AoS, plus code and analysis (technical paper)
Integrate outcomes of collaboration in UNICEF pipelines
Implement and deliver coded and documented automated data pipelines for the Ahead of the Storm analytics platform
Algorithm development
Develop, document, and present the machine learning models and GenAI solutions used to deliver analytics results for Ahead of the Storm
Assist team in building MVP
Implement country-specific, tailored, coded and documented automated data pipelines for the Ahead of the Storm analytics platform

Skills

University degree in Data Science, Computer Science, Applied Mathematics, and related fields
5+ years proven experience in developing, training, and deploying machine learning models, particularly for supply chain optimization or similar logistical challenges.
Expertise in supervised, unsupervised, and reinforcement learning techniques.
Proficiency in Python.
Experience using machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Strong experience with deploying machine learning models in cloud environments, particularly Microsoft Azure.
Familiarity with Azure Machine Learning, Azure Functions, Azure Databricks, or similar services.
Experience building and deploying GenAI services, including RAG, RPA, and agentic.
Expertise wrangling big data and large datasets, including data cleaning, feature engineering, data extraction, and data visualization.
Experience with SQL and NoSQL databases.
Excellent problem-solving and troubleshooting skills.
Strong communication and collaboration skills to work effectively with cross-functional teams.
Previous experience building robust data pipelines using queueing and stream processing, asynchronous patterns, parallelization, e.g., with PySpark, etc.
Understanding of vaccine supply chains, including distribution strategies, cold chain logistics, and challenges faced at subnational levels.
Ability to translate complex supply chain problems into data-driven models and solutions.
Previous experience in similar consulting roles, particularly in global health or public health contexts, is highly desirable.

Benefits

Any emergent / unforeseen duty travel and related expenses will be covered by UNICEF.
At the time the contract is awarded, the selected candidate must have in place current health insurance coverage.
Payment of professional fees will be based on submission of agreed satisfactory deliverables. UNICEF reserves the right to withhold payment in case the deliverables submitted are not up to the required standard or in case of delays in submitting the deliverables on the part of the consultant.
UNICEF offers reasonable accommodation for consultants/individual contractors with disabilities. This may include, for example, accessible software, travel assistance for missions or personal attendants.

Company Overview

UNICEF works in some of the world’s toughest places, to reach the world’s most disadvantaged children. To save their lives. To defend their rights. It was founded in 2015, and is headquartered in New York, New York, USA, with a workforce of 10001+ employees. Its website is https://agora.unicef.org/.

Company H1B Sponsorship

UNICEF has a track record of offering H1B sponsorships, with 1 in 2023, 2 in 2020. Please note that this does not guarantee sponsorship for this specific role.

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

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