Key Responsibilities
 Card Fraud Model Development & Optimization: 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.
 Model Hosting & Deployment: Deploy models using SAS SFD and integrate with production systems. Ensure seamless hosting and scalability of models across multiple environments.
 MLOps & Automation: Establish and maintain MLOps pipelines for continuous integration, deployment, and monitoring of fraud models. Automate model retraining and performance tracking processes.
 Evaluation & Testing: Conduct rigorous model validation, stress testing, and performance benchmarking. Collaborate with fraud operations teams to ensure models meet business and regulatory requirements.
 Collaboration & Stakeholder Management: 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.
Required Skills & Qualifications
 Education: Master’s or Ph.D. in Data Science, Statistics, Computer Science, or related field.
 Technical Skills: Strong proficiency in SAS (including SAS SFD), Python, and SQL.
Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-
learn). Hands-on experience with MLOps tools and practices (e.g., MLflow,Kubeflow, CI/CD pipelines). Deep understanding of card fraud detection techniques and transaction data.
 Experience: 7+ years in data science roles, with at least 3 years in card fraud
modeling. Proven track record of deploying models in production environments.
 Soft Skills: Strong analytical and problem-solving skills. Excellent communication
and stakeholder management abilities.
Preferred Qualifications
 Experience in banking or payments industry.
 Familiarity with cloud platforms (AWS, Azure, GCP) for model hosting and
deployment.
 Knowledge of regulatory compliance in fraud risk management.