Senior Fraud Data Scientist
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Key skills for this role
About the Role
· Card Fraud Model Development & Optimization: Design, develop, and optimize card fraud detection models for issuing and acquiring portfolios. Implement advanced statistical, ma.
Key Skills for This Role
Responsibilities
- Design, develop, and optimize card fraud detection models for issuing and acquiring portfolios
- Implement advanced statistical, machine learning, and AI techniques to improve fraud detection accuracy and minimize false positives
- Deploy models using SAS SFD and integrate with production systems
- Establish and maintain MLOps pipelines for continuous integration, deployment, and monitoring of fraud models
- Automate model retraining and performance tracking processes
- Conduct rigorous model validation, stress testing, and performance benchmarking
- Collaborate with fraud operations teams to ensure models meet business and regulatory requirements
- Partner with fraud risk, data engineers, IT and business teams to deliver end to end solutions
- Communicate insights and recommendations to senior management and business stakeholders
Requirements
- 7 12 years of experience in fraud data science or related field
- Experience in card fraud model development and optimization for issuing and acquiring portfolios
- Proficiency in advanced statistical, machine learning, and AI techniques
- Experience with SAS SFD for model hosting and deployment
- Experience establishing MLOps pipelines for continuous integration, deployment, and monitoring
- Strong model validation, stress testing, and performance benchmarking skills
- Ability to collaborate with fraud risk, data engineers, IT, and business teams
Full Job Posting
Key Responsibilities
- Design, develop, and optimize card fraud detection models for issuing and acquiring portfolios
- Implement advanced statistical, machine learning, and AI techniques to improve fraud detection accuracy and precision and minimizing false positives
- Deploy models using SAS SFD and integrate with production systems
- Establish and maintain MLOps pipelines for continuous integration, deployment, and monitoring of fraud models
- Automate model retraining and performance tracking processes
- Conduct rigorous model validation, stress testing, and performance benchmarking
- Collaborate with fraud operations teams to ensure models meet business and regulatory requirements
- Partner with fraud risk, data engineers, IT and business teams to deliver end to end solutions
- Communicate insights and recommendations to senior management and business stakeholders
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