Lead Global Risk ML Scientist & AI-Driven Fraud Architect

Global Payments Inc.; Alpharetta (GA); On-site; USD 140,000 - 190,000; Full time

Job summary

Global Payments Inc. is seeking a hands-on Lead Risk Data Scientist & ML Engineer to own the full lifecycle of fraud detection and risk models, aligning with regulatory requirements and operational needs across Risk, Compliance, and Technology teams.

You will drive model performance from design and validation through production deployment, building scalable data pipelines and AI-assisted analyst workflows in a fast-moving, high-impact team.

Qualifications

• 7+ years in data science, ML or MLOps.
• Proven experience deploying detection models in production.
• Hands-on with AI-assisted workflows and agentic frameworks (pilot deployments).
• Experience in regulated financial services or fintech preferred.
• Exposure to model risk management frameworks (SR 11-7).

Responsibilities

• Own end-to-end ML model lifecycle: design, validate, deploy, and serve in production.
• Monitor model performance and refine using production data.
• Ensure models are explainable and auditable per regulatory expectations.
• Design scalable batch and real-time data pipelines for model development and serving.
• Lead AI-assisted analyst workflows using LLMs and agentic frameworks.
• Define governance for model development, validation, and change management.
• Partner with Fraud Operations, Compliance, and Technology teams.

Skills

Python

SQL

MLOps

Git / CI-CD

Model Monitoring

Databricks

Snowflake

AWS SageMaker

AWS Glue

AWS Lambda

EventBridge

LangChain

LLMs

AI Workflows

Tools

Databricks

Snowflake

SageMaker

Glue

Lambda

EventBridge

LangChain

Claude

GPT

Job description

Global Payments Inc. is seeking a hands-on Lead Risk Data Scientist & ML Engineer to own the full lifecycle of fraud detection and risk models, aligning with regulatory requirements and operational needs across Risk, Compliance, and Technology teams.

You will drive model performance from design and validation through production deployment, building scalable data pipelines and AI-assisted analyst workflows in a fast-moving, high-impact team.