Data Scientist - Fraud Analytics

Tasks

• Analyze large datasets
• Collaborate with fraud risk rule teams
• Conduct feature engineering
• Deploy fraud detection models
• Detect behavioral anomalies
• Develop fraud detection models
• Ensure regulatory compliance
• Evaluate model performance using metrics
• Identify emerging threats
• Implement and test models in UAT
• Monitor model performance
• Monitor performance post-deployment
• Optimize fraud detection strategies
• Perform fraud pattern analysis
• Research emerging fraud typologies and techniques
• Support model governance documentation
• Translate insights into actionable controls
• Tune machine learning models
• Validate fraud detection models

Perks/Benefits

• High-impact projects

Skills/Tech-stack

Decision Trees | False Positive | False Positive Rate | Feature Engineering | Fraud Detection | Gradient boosting | Logistic Regression | Machine Learning | Model Evaluation | Model Monitoring | Model Tuning | Neural Networks | Precision Recall AUC | Precision-recall | Python | R | Random Forests | SQL | Statistical Analysis | UAT