UNICEF is looking to solve problems related to machine learning, artificial intelligence, data analysis, and information extraction, particularly in the areas of vaccine stockout, data extraction from unstructured documents, and geospatial analytics.
Requirements
- 5+ years proven experience in developing, training, and deploying machine learning models
- 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
Responsibilities
- Maintain and optimize the vaccine stockout machine learning model already trained for UNICEF’s Program Group Immunization Division
- Continue to enhance methods for large-scale data and information extraction from diverse and unstructured document sources
- 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
- Provide technical advice on AI/ML approaches
- Build reproducible workflows and contribute to machine learning and GenAI knowledge transfer within the team
Other
- University degree in Data Science, Computer Science, Applied Mathematics, and related fields
- Completed profile in UNICEF's e-Recruitment system
- Upload copy of academic credentials
- Financial proposal that includes costs per deliverable and total lump-sum for the assignment
- Travel costs and daily subsistence allowance, if internationally recruited or travel is required
