E T Consultant
Job #:
req38942
Organization:
World Bank
Sector:
Other
Grade:
EC1
Term Duration:
1 year 0 months
Recruitment Type:
Local Recruitment
Location:
Washington, DC,United States
Required Language(s):
English
Preferred Language(s):
Closing Date:
11/6/2026 (MM/DD/YYYY) at 11:59pm UTC
Description
The DIP in EAP is expanding its use of high-frequency, nontraditional data sources and AI-enabled workflows to support economic monitoring, poverty analysis, price data collection, and policy surveillance across the region. These activities require specialized expertise in machine learning, large language models (LLMs), data engineering, web data acquisition, quality assurance systems, and statistical production pipelines.
Jeronimo Luza has been providing critical support in the development and operationalization of these systems, including text-processing pipelines, product classification models, source discovery workflows, data quality controls, dashboard development, and country-level implementation of real-time monitoring tools. Recent work has included the deployment and validation of large-scale classification systems, source-search automation, country testing, dashboard enhancements, and survey-to-price integration workflows.
Objective
The objective of the assignment is to provide specialized technical support for the design, implementation, maintenance, and scaling of data science and AI-enabled monitoring systems that generate timely indicators and analytical products to support operational and analytical work programs.
Duties and Responsibilities
1. Design, develop, and maintain machine-learning and AI-based pipelines for economic and social monitoring.
2. Support large-scale classification and extraction systems for structured and unstructured data, including product, price, news, and survey datasets.
3. Develop and refine methodologies for entity classification, taxonomy mapping, semantic search, embeddings-based approaches, and LLM-assisted workflows.
4. Implement quality assurance procedures, validation protocols, and error-detection systems to improve the accuracy and reliability of analytical outputs.
5. Support the acquisition, processing, integration, and harmonization of data from web sources, administrative systems, and survey datasets.
6. Develop automated dashboards, monitoring tools, and analytical products used for operational decision-making and policy analysis.
7. Conduct country-level testing, validation, and deployment of monitoring systems and associated workflows.
8. Prepare technical documentation, methodological materials, and user guidance for developed systems.
9. Collaborate with economists, data scientists, country teams, and operational staff to translate business requirements into technical solutions.
10. Provide troubleshooting, maintenance, and continuous improvement support for production systems.
Expected Deliverables
• Operational AI and data-processing pipelines supporting real-time monitoring activities.
• Enhanced classification and extraction models for economic datasets.
• Country-level implementation and validation reports.
• Data quality assessment and monitoring products.
• Automated dashboards and reporting tools.
• Technical documentation and reproducible analytical workflows.
• Support materials and analytical inputs for regional and country work programs.
Selection Criteria
The assignment requires specialized knowledge of the existing architecture, data pipelines, AI workflows, and operational systems currently supporting the unit's work program. Jeronimo Luza possesses demonstrated expertise in these systems and has made substantial contributions to their development, testing, deployment, and maintenance. His knowledge of the underlying technical infrastructure, methodologies, and ongoing work program requirements enables continuity of operations and minimizes implementation risks associated with the transition from ST to ET status.
Qualifications
• Advanced degree in economics, statistics, computer science, data science, or a related field.
• Demonstrated experience in machine learning, data engineering, and applied analytics.
• Strong programming skills in Python and associated data-science libraries.
• Experience with LLMs, natural language processing, embeddings, semantic search, and AI-assisted workflows.
• Experience developing reproducible analytical pipelines and automated reporting systems.
• Strong communication and collaboration skills.
