Lead Risk Data Scientist & ML Engineer at Global Payments in Alpharetta, GA

Job description

Job Description Job Posting Description Ready to take your career global? Make your mark at one of the biggest names in payments. We are seeking a hands-on Lead Risk Data Scientist & ML Engineer to own the full lifecycle of fraud detection and risk models. This role combines deep data science expertise with practical AI/agentic workflow experience and the infrastructure knowledge needed to ship at scale. What You’ll Own In this role, you'll own the end-to-end delivery of detection models and AI-assisted workflows that power fraud, credit, and AML risk operations. You'll drive model performance through the full lifecycle, from design and validation through production deployment and continuous optimization. You'll translate regulatory requirements and operational needs into detection strategies and technical execution plans, partner across Risk, Compliance, and Technology to ensure alignment, and lead project teams through complex, ambiguous detection challenges. This is a hands-on role that combines deep technical leadership with pragmatic problem-solving in a small, high-impact team. Model Development & Deployment (End-to-End) Own the full lifecycle of ML models: design, development, validation, deployment, and serving in production Lead model performance monitoring and continuous refinement using production data and investigation outcomes Ensure models are explainable, auditable, and aligned with regulatory expectations Design and oversee scalable batch and real-time data pipelines supporting model development and serving AI & Agentic Workflows Design and deploy AI-assisted analyst workflows using LLMs and agentic frameworks Guide the development of agent-based systems that augment human decision-making in risk operations Work at the pilot/proof-of-concept stage, establishing best practices for scale Detection Strategy & Performance Define and refine detection strategies based on emerging fraud patterns and regulatory requirements Maintain and monitor key performance metrics (precision, recall, false positives, alert quality) Influence tradeoff decisions between detection coverage, operational cost, and false positive rates Governance & Regulatory Alignment Define governance standards for model development, validation, documentation, and change management Ensure compliance with regulatory expectations (BSA/AML, OFAC, FinCEN, SR 11-7) Partner with Model Risk Management and Compliance to support validation and regulatory reviews Cross-Functional Partnership Serve as the primary technical partner to Fraud Operations, Compliance, and Technology teams Translate regulatory and operational requirements into technical execution plans Drive alignment across teams to enable effective detection capability implementation Team Leadership & Project Ownership Lead cross-functional project teams through ML model and AI workflow development, from conception to deployment Establish clear priorities, performance expectations, and delivery accountability for project work Provide technical guidance and mentorship to data scientists and engineers executing on risk initiatives Build and strengthen team capabilities across detection modeling, data engineering, and AI/agentic systems What you’ll bring: Experience 7+ years in data science, machine learning or MLOps Proven experience developing, deploying, and maintaining detection models (fraud, AML, or credit risk) in production environments Hands-on experience with AI-assisted workflows, LLMs, and agentic frameworks (including pilot-stage deployments) Experience in regulated financial services or fintech environments preferred Exposure to model risk management frameworks (SR 11-7) and regulatory interactions Technical & Domain Expertise Strong proficiency in Python and SQL MLOps experience: Git, GitHub Actions, CI/CD practices, model monitoring, retraining pipelines, infrastructure automation Hands-on experience with data science platforms (Databricks, Snowflake, AWS SageMaker) AWS ecosystem experti