Embedded Intern Jobs in Washington (NOW HIRING)

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. 
Document model assumptions, feature engineering decisions, and evaluation metrics in a clear and reproducible manner. 
Pipeline Integration 

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. 
Agile Delivery & Stakeholder Engagement 

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