Senior fraud data scientist

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.