Lead Risk Data Scientist & ML Engineer

Global Payment Holding Company•Alpharetta, GA

About The Position

Global Payments is seeking a hands-on Lead Risk Data Scientist & ML Engineer to own the end-to-end lifecycle of fraud detection and risk models, as well as AI-assisted workflows supporting fraud, credit, and AML risk operations. The role spans model design, validation, production deployment, monitoring, and continuous optimization, with responsibility for scalable batch and real-time data pipelines and for ensuring models are explainable, auditable, and aligned with regulatory expectations. The role also designs and deploys AI-assisted analyst workflows using LLMs and agentic frameworks, initially at pilot or proof-of-concept stage, and guides practices for scaling them. It defines detection strategies and performance measures, balances detection coverage with operating costs and false positives, and establishes governance standards for model development, documentation, validation, and change management. As the primary technical partner to Fraud Operations, Compliance, and Technology, the role translates regulatory and operational requirements into technical plans and leads cross-functional project teams from conception through delivery. It provides technical guidance and mentorship to data scientists and engineers and helps build capabilities in detection modeling, data engineering, and AI/agentic systems. The position is intended for a resourceful, execution-focused technical leader comfortable working in a small, fast-moving team and navigating ambiguity.

Requirements

At least 7 years of experience in data science, machine learning, or MLOps.
Proven experience developing, deploying, and maintaining production detection models in fraud, AML, or credit risk.
Hands-on experience with AI-assisted workflows, LLMs, and agentic frameworks, including pilot-stage deployments.
Strong proficiency in Python and SQL.
MLOps experience with Git, GitHub Actions, CI/CD practices, model monitoring, retraining pipelines, and infrastructure automation.
Hands-on experience with data science platforms such as Databricks, Snowflake, or AWS SageMaker.
Expertise in the AWS ecosystem, including SageMaker, Glue, Lambda, EventBridge, and related services.
Familiarity with foundational models such as Claude and GPT and agent orchestration tools such as AWS AgentCore and LangChain.
Understanding of fraud typologies, AML transaction-monitoring methodologies, and detection-system design.
Ability to lead project teams through ambiguous problems and drive clarity, structure, and delivery.
Ability to collaborate across Risk, Compliance, and Technology and translate strategy into action.

Nice To Haves

Experience in regulated financial services or fintech environments.
Exposure to model risk management frameworks such as SR 11-7 and regulatory interactions.

Responsibilities

Own the full lifecycle of machine-learning models, including design, development, validation, deployment, and production serving.
Monitor model performance and continuously refine models 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 for model development and serving.
Design and deploy AI-assisted analyst workflows using LLMs and agentic frameworks.
Guide development of agent-based systems that augment human decision-making in risk operations, including pilot-stage work and practices for scaling.
Define and refine detection strategies based on emerging fraud patterns and regulatory requirements.
Maintain and monitor metrics including precision, recall, false positives, and alert quality.
Influence tradeoff decisions among detection coverage, operational cost, and false-positive rates.
Define governance standards for model development, validation, documentation, and change management.
Ensure alignment with regulatory expectations, including BSA/AML, OFAC, FinCEN, and SR 11-7.
Partner with Model Risk Management and Compliance on validation and regulatory reviews.
Serve as the primary technical partner to Fraud Operations, Compliance, and Technology teams.
Translate regulatory and operational requirements into technical execution plans.
Lead cross-functional project teams developing ML models and AI workflows from conception through deployment.
Establish priorities, performance expectations, and delivery accountability for project work.
Provide technical guidance and mentorship to data scientists and engineers working on risk initiatives.
Build team capabilities in detection modeling, data engineering, and AI/agentic systems.