Overview
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.
Key Responsibilities
- 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 that 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.
- Produce technical documentation covering the model architecture, pipeline integration design, and operational guidelines.
- Prepare a final presentation summarizing findings, methodology, and recommendations.
Required Experience
0–6 years of relevant professional experience
Qualifications
Currently enrolled in, or in the final year of postgraduate program in Engineering.
