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.
- Design and train a lightweight, interpretable anomaly detection model.
- 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 Undergraduate program in Engineering.
- 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.
