WBG Pioneer -Financial Data Engineering Intern

World Bank Group•Washington, DC

1d•Onsite

About The Position

The WBG Pioneers program offers students a high-impact learning experience in global development. The Financial Engineering unit (ITSFE) supports the World Bank Group's core financial operations by designing and maintaining data pipelines, reporting systems, and analytical tools for critical financial instruments like IDA replenishments and disbursements. This role focuses on leveraging machine learning for dynamic data quality controls in complex, high-volume data environments, moving beyond traditional static validation logic. ITSFE is seeking an intern to help design and prototype a machine learning-based anomaly detection capability integrated into IDA's data pipelines, working at the intersection of data engineering, financial operations, and applied AI.

Requirements

Currently enrolled in, or in the final year of Undergraduate program in Engineering.
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.

Nice To Haves

Experience with Azure cloud services (e.g., Azure Data Factory, Azure Machine Learning, or Azure Databricks).

Responsibilities

Apply machine learning algorithms to data pipelines handling IDA replenishments and disbursements to automatically flag anomalies, missing data patterns, and forecasting errors.
Conduct a structured analysis of historical IDA data flows 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.
Document model assumptions, feature engineering decisions, and evaluation metrics.
Integrate the trained model into an automated data pipeline leveraging Azure cloud services.
Develop alerting or flagging mechanisms to surface detected anomalies.
Ensure the solution adheres to WBG data governance standards and security protocols.
Participate fully in ITSFE's Agile ceremonies.
Present progress and prototype demos to unit stakeholders.
Collaborate with data engineers, financial analysts, and technical leads to refine requirements and validate model outputs.
Produce technical documentation covering the model architecture, pipeline integration design, and operational guidelines.
Prepare a final presentation summarizing findings, methodology, and recommendations.