Data Scientist (Evaluation)

World Bank Group•Washington, DC

1d•Onsite

About The Position

The Data Scientist will support the Global Environment Facility Independent Evaluation Office (GEF IEO), which is functionally independent and administratively supported by the World Bank. The IEO undertakes independent strategic evaluations, sets minimum evaluation requirements, oversees the quality of evaluation systems, and shares evaluative evidence across the GEF. The position is based in Washington, DC, and is a World Bank Group appointment. The role leads the development, integration, management, and analysis of complex datasets and applies data science, AI, machine learning, and geospatial methods to IEO evaluations and analytical work. It provides methodological and technical input to evaluation teams, leads analytical studies and working papers, and develops reproducible data systems, visualizations, guidance, and training resources. The Data Scientist also builds relationships with technical partners and helps support responsible, transparent, and reproducible use of computational evidence.

Requirements

Master's degree in computer science, data science, statistics, economics, information management, environmental sciences, public policy, finance, business administration, or another relevant discipline with a strong quantitative and analytical component.
Advanced training in statistics, econometrics, programming, machine learning, or related computational methods.
At least five years of relevant professional experience applying data science, advanced analytics, AI, machine learning, or related computational methods to complex research, evaluation, policy, or development questions, including work with large and heterogeneous datasets and reproducible analytical workflows.
Demonstrated experience applying AI and machine-learning frameworks, including natural language processing, large language models and prompt engineering, topic modelling, information extraction, classification, or related approaches; ability to assess the validity, limitations, and appropriate use of computational evidence.
Ability to independently design analytical approaches, translate research or evaluation questions into quantitative and computational methods, and integrate evidence across data sources and methodological approaches.
Strong conceptual, research, and analytical skills, including interpreting complex findings, identifying policy and evaluation significance, and communicating methodological limitations clearly.
Advanced proficiency in Python, R, SQL, or comparable programming environments, with experience in databases, data-management systems, APIs, and reproducible analytical workflows.
Strong proficiency in data visualization and communication using Tableau, Power BI, D3.js, or comparable tools, and ability to communicate complex results to technical and non-technical audiences.
Ability to provide technical guidance and quality assurance to multidisciplinary teams and contribute to methodological standards, guidelines, or institutional analytical practices.
Excellent interpersonal skills and ability to collaborate across disciplines, institutions, countries, and cultures and build productive relationships with internal and external technical partners.
Excellent written and oral communication skills in English, including preparing high-quality analytical and methodological products and presenting complex technical findings clearly.
Ability to manage multiple complex assignments with substantial autonomy, exercise sound judgment in setting priorities and resolving analytical problems, and deliver high-quality results within agreed objectives and deadlines.

Nice To Haves

A PhD or equivalent advanced research experience.
Experience evaluating environmental, climate, biodiversity, or sustainable-development interventions.
Experience with geospatial analysis and GIS platforms such as QGIS or ArcGIS, and/or programmatic geospatial analysis.
Experience integrating remote-sensing, geospatial, socioeconomic, and other data sources.
Knowledge of an additional language, such as Chinese, French, Spanish, or Portuguese.
Experience working in fragile, conflict-affected, and violent (FCV) contexts is encouraged to be highlighted where applicable.

Responsibilities

Lead the development, management, integration, and analysis of complex datasets from internal and external sources, and develop robust, reproducible, maintainable code, analytical pipelines, and data systems with appropriate data-quality standards and documentation.
Design and apply AI and advanced data-science approaches to extract, process, integrate, and analyze large, heterogeneous sources, and assess the robustness and limitations of the resulting evidence.
Develop, test, and adapt machine-learning and AI techniques, including approaches integrating text, satellite imagery, geospatial data, and socioeconomic and environmental databases.
Provide methodological and technical input to IEO evaluations, identifying opportunities to apply AI, machine learning, geospatial analysis, and other data-science methods to evaluation design, evidence collection, triangulation, and analysis.
Work with evaluation teams to ensure advanced methods address evaluation questions and that results are interpreted in institutional, socioeconomic, and environmental contexts.
Build and maintain working relationships with data producers, curators, engineers, researchers, and technical partners; represent IEO in technical exchanges and identify collaboration and data-access opportunities.
Contribute to open and reproducible data documentation, cataloguing, management, and dissemination, promoting good practices in curation, metadata, interoperability, transparency, and reproducibility.
Design and oversee dashboards, interactive analytical products, and advanced visualizations.
Develop technical guidelines, methodological protocols, reference materials, and training resources for applying data science, AI, machine learning, and geospatial methods to evaluation.
Lead analytical working papers and technical studies, including methodological design, quantitative and computational analysis, interpretation, visualization, and presentation of results and methodological limitations.
Undertake other analytical and methodological assignments, exercising independent judgment to define approaches, resolve complex technical issues, and deliver outputs within agreed objectives and timelines.