Machine Learning Engineer for Fraud Detection System

Skills Required

Description

Building a fraud detection system requires models that can quickly identify unusual behavior while minimizing false positives. The engineer will leverage Scikit-learn and PyTorch to design and train algorithms capable of handling large transaction datasets.

Feature engineering will be essential. By extracting meaningful signals from raw data, the system can better distinguish between legitimate and fraudulent activity. This step will directly impact accuracy and reliability.

SQL will be used to query and organize historical data, while APIs will allow real-time integration of the detection system into existing platforms. Speed and efficiency in data handling are key for fraud scenarios.

Model evaluation will involve testing precision, recall, and overall performance under real-world conditions. Continuous updates will be required to adapt to evolving fraud tactics.

The end result should be a robust fraud detection pipeline that safeguards transactions, improves trust, and reduces financial risk for the business and its customers.