WBG Pioneer -Financial Data Engineering Intern

The WBG Pioneer internship offers undergraduate and postgraduate students hands-on experience in global development. This internship focuses on designing and prototyping a machine learning–based anomaly detection capability integrated into IDA's financial data pipelines, contributing to global development finance through cutting-edge technology.

Responsibilities

• 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.
• 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.
• 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.

Requirements

• 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

Skills

• Probability distributions
• Time-Series Analysis
• Anomaly Detection
• Machine Learning
• Scikit Learn
• TensorFlow
• PyTorch
• Python Programming
• Data Manipulation
• Pandas
• NumPy
• SQL
• Cloud Data Engineering
• Azure
• Statistical Analysis
• Financial Data Engineering

Languages

English