Fraud Data Science

📋 Description

• Data analysis and modeling: Explore large datasets to uncover fraud patterns.
• Model development and validation: Build and validate models for imbalanced fraud data.
• Feature engineering: Create derived variables to boost model performance.
• Cross-functional collaboration: Work with engineering, product, and operations teams.
• Monitoring and iteration: Track model performance and iterate on changes.
• Research and innovation: Stay up to date on ML techniques for fraud detection.

🎯 Requirements

• Degree in Data Science, Statistics, Mathematics, CS with strong programming skills.
• 1-2 years in Data Science or Data/Business Analytics with ML knowledge.
• 2+ years of Python and SQL experience.
• Knowledge of fraud detection, anomaly detection, or imbalanced-data modeling.
• Strong analytical skills and ability to extract actionable insights from data.
• AWS Services knowledge is a plus.