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WBG Pioneer -Financial Data Engineering Intern Job #: req37642 Organization: World Bank Grade: T3 Location: Washington, DC,United States Hiring Manager: Michael Fedesin Required Language(s): English Preferred Language(s)…
Skills: Machine Learning, Python, SQL, Azure, Anomaly Detection
Washington, District of Columbia, United States · On-site
WBG Pioneer -Financial Data Engineering Intern Job #: req37644 Organization: World Bank Grade: T4 Location: Washington, DC,United States Hiring Manager: Michael Fedesin Required Language(s): English Preferred Language(s)…
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Design and prototype a machine learning-based anomaly detection capability integrated into IDA's financial data pipelines. The role involves analyzing historical data flows and developing alerting mechanisms within an Agile delivery model.
Candidates must be currently enrolled in or in the final year of an undergraduate engineering program with a strong statistical background. Proficiency in Python, SQL, and machine learning libraries, along with familiarity with Azure cloud services, is required.
WBG Pioneer -Financial Data Engineering Intern
| Job #: | req37642 |
| Organization: | World Bank |
| Grade: | T3 |
| Location: | Washington, DC,United States |
| Hiring Manager: | Michael Fedesin |
| Required Language(s): | English |
| Preferred Language(s): | |
| Closing Date: | 8/12/2026 (11:59pm UTC) |
DescriptionWBG Pioneers, the World Bank Group’s Internship Program, offers undergraduate and postgraduate students a high impact learning experience at the heart of global development. Participants gain hands on experience in a diverse and dynamic environment, contribute fresh perspectives and innovative ideas, and connect with international professionals working to end poverty on a livable planet. WBG Pioneer The Financial Engineering unit (ITSFE) within ITS supports the World Bank Group's core financial operations by designing and maintaining data pipelines, reporting systems, and analytical tools that underpin critical financial instruments — including IDA replenishments and disbursements. IDA, the World Bank's fund for the world's poorest countries, operates at massive scale and with the highest standards of data integrity. Any error or anomaly in the underlying data flows can cascade into financial reports relied upon by internal stakeholders, donor governments, and partner institutions. Traditional data engineering in this space relies on static, rule-based validation logic — an approach that is increasingly insufficient in the face of complex, high-volume, and evolving data environments. Machine learning offers a pathway to dynamic, adaptive data quality controls that can detect anomalies, flag missing data, and identify forecasting inconsistencies before they reach downstream systems. ITSFE is seeking a Pioneer intern to help design and prototype a machine learning–based anomaly detection capability integrated directly into IDA's data pipelines. This role sits at the intersection of data engineering, financial operations, and applied AI — offering a rare opportunity to contribute to global development finance through cutting-edge technology. Duties and Responsibilities The intern will apply machine learning algorithms to data pipelines handling IDA replenishments and disbursements to automatically flag anomalies, missing data patterns, and forecasting errors before they propagate into downstream financial reports. The work will be embedded within ITSFE's Agile delivery model, ensuring that outputs are iterative, demonstrable, and production-oriented. Data Analysis & Model Development • Conduct a structured analysis of historical IDA data flows, including replenishment cycles, disbursement patterns, and associated metadata, to identify key signals and failure modes relevant to anomaly detection. • Design and train a lightweight, interpretable anomaly detection model using appropriate machine learning approaches (e.g., Isolation Forest, Autoencoders, or statistical process control methods), calibrated to the sensitivity requirements of financial data. • Integrate the trained model into an automated data pipeline leveraging Azure cloud services (e.g., Azure Data Factory, Azure Machine Learning, or Azure Databricks), in alignment with ITSFE's existing infrastructure. • Develop alerting or flagging mechanisms that surface detected anomalies to data engineers and financial analysts in a timely and actionable format. • Ensure the solution adheres to WBG data governance standards and security protocols. • Participate fully in ITSFE's Agile ceremonies, including sprint planning, daily standups, sprint reviews, and retrospectives. • Present progress and prototype demos to unit stakeholders, showcasing how predictive capabilities improve data governance and reduce manual validation overhead. • Collaborate with data engineers, financial analysts, and technical leads to refine requirements and validate model outputs against real-world expectations. Documentation & Knowledge Transfer • Produce technical documentation covering the model architecture, pipeline integration design, and operational guidelines for handoff to the engineering team. • Prepare a final presentation summarizing findings, methodology, and recommendations for scaling or productionizing the solution. Working Environment The intern will be embedded within ITSFE's Financial Engineering team and will work in a mature Agile environment. This is not a standard analytics rotation. The intern will be an active contributor to an AI-enabled delivery model, working alongside experienced data engineers and financial technologists, and will have direct visibility into how technology decisions shape global development finance operations. The role offers exposure to: • Production-grade cloud data infrastructure at the World Bank Group • Real-world application of machine learning in a regulated, high-stakes financial setting • Agile product delivery with demonstrable, sprint-level outcomes • A multidisciplinary team spanning engineering, finance, and development policy Selection Criteria• Candidates must be currently enrolled in, or in the final year of Undergraduate program in Engineering. • Candidates must have 0–6 years of relevant professional experience • Academic background must align with the requirements outlined in the job description • Strong statistical background, including understanding of probability distributions, time-series analysis, and anomaly detection methodologies. • Hands-on experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch. • Proficiency in Python and data manipulation tools (pandas, NumPy, SQL). • Familiarity with cloud-based data engineering concepts, preferably on Azure. • Intellectually curious with a genuine interest in applying AI to high-impact, real-world financial systems. • Demonstrated interest in development work and the World Bank Group’s mission • Strong analytical, research, and problem-solving skills Note: Please limit your applications to a maximum of three positions. Applications exceeding this limit will not be considered. WBG Culture Attributes: The World Bank Group values diversity and encourages all qualified candidates who are nationals of World Bank Group member countries to apply, regardless of gender, gender identity, religion, race, ethnicity, sexual orientation, or disability. Sub-Saharan African nationals, Caribbean nationals, and female candidates are strongly encouraged to apply. |
The World Bank is a vital source of financial and technical assistance to developing countries around the world. Our vision is to create a world free of poverty on a livable planet. We are not a bank in the common sense; we are made up of two unique development institutions owned by 189 member countries: the International Bank for Reconstruction and Development (IBRD) and the International Development Association (IDA). Each institution plays a different but collaborative role in advancing the vision of inclusive and sustainable globalization. The IBRD aims to reduce poverty in middle-income and creditworthy poorer countries, while IDA focuses on the world's poorest countries. Their work is complemented by that of the International Finance Corporation (IFC), Multilateral Investment Guarantee Agency (MIGA) and the International Centre for the Settlement of Investment Disputes (ICSID). Together, we provide low-interest loans, interest-free credits and grants to developing countries for a wide array of purposes that include investments in education, health, public administration, infrastructure, financial and private sector development, agriculture and environmental and natural resource management.
Offices: 1818 H Street, NW, Washington, DC 20433, US
