Lead Risk Data Scientist & ML Engineer

Global Payment Holding Company; Alpharetta (GA); On-site; USD 180,000 - 240,000; Full time

Job summary

Global Payment Holding Company is seeking a senior risk ML leader to own end-to-end detection models and AI-assisted workflows for fraud, AML, and credit risk. This onsite role in Alpharetta, GA focuses on scalable pipelines, explainable models, and regulatory alignment.

You will drive model performance, monitor production outcomes, and mentor data scientists while collaborating with Compliance and Tech teams to meet SR 11-7 standards.

Qualifications

• 7+ years in data science, machine learning, or MLOps.
• Experience deploying detection models (fraud, AML, or credit risk) in production.
• Hands-on with AI-assisted workflows, LLMs, and agentic frameworks, including pilot-stage deployments.

Responsibilities

• Own the end-to-end delivery of detection models and AI-assisted workflows that support fraud, credit, and AML risk operations.
• Drive model performance across the full lifecycle, including design, validation, production deployment, and continuous optimization.
• Translate regulatory requirements and operational needs into detection strategies and technical execution plans.
• Ensure models are explainable, auditable, and aligned with regulatory expectations.
• Lead model performance monitoring and continuous refinement using production data and investigation outcomes.
• 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, including pilot and proof-of-concept deployments.
• Guide the development of agent-based systems that augment human decision-making in risk operations.

Skills

Python

SQL

Git

GitHub Actions

CI/CD

Databricks

Snowflake

AWS SageMaker

Glue

Lambda

EventBridge

Claude

GPT

AWS AgentCore

LangChain

Tools

Databricks

Snowflake

AWS SageMaker

Claude

GPT

AWS AgentCore

LangChain

Job description

Global Payment Holding Company is building and scaling detection intelligence to support fraud, credit, and AML risk operations in a regulated payments environment. In this onsite role in Alpharetta, GA, you will lead the end-to-end lifecycle of risk machine learning models and design AI-assisted, LLM and agentic workflows that help analysts make better decisions using governed, explainable systems.

What you’ll do

• Own the end-to-end delivery of detection models and AI-assisted workflows that support fraud, credit, and AML risk operations.
• Drive model performance across the full lifecycle, including design, validation, production deployment, and continuous optimization.
• Translate regulatory requirements and operational needs into detection strategies and technical execution plans.
• Ensure models are explainable, auditable, and aligned with regulatory expectations.
• Lead model performance monitoring and continuous refinement using production data and investigation outcomes.
• 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, including pilot and proof-of-concept deployments.
• Guide the development of agent-based systems that augment human decision-making in risk operations.
• Define and refine detection strategies based on emerging fraud patterns and regulatory requirements.
• Maintain and monitor key performance metrics including precision, recall, false positives, and alert quality.
• Influence tradeoff decisions between detection coverage, operational cost, and false positive rates.
• Define governance standards for model development, validation, documentation, and change management.
• Ensure compliance with regulatory expectations including BSA/AML, OFAC, FinCEN, and SR 11-7.
• Partner with Model Risk Management and Compliance to support validation and regulatory reviews.
• Serve as a primary technical partner to Fraud Operations, Compliance, and Technology teams.
• Lead cross-functional project teams from conception through deployment for ML model and AI workflow development, setting priorities, performance expectations, and delivery accountability.
• Provide technical guidance and mentorship to data scientists and engineers working on risk initiatives.
• Build and strengthen team capabilities across detection modeling, data engineering, and AI/agentic systems.

What you bring

• 7+ years in data science, machine learning, or MLOps.
• Proven experience developing, deploying, and maintaining detection models (fraud, AML, or credit risk) in production.
• Hands-on experience with AI-assisted workflows, LLMs, and agentic frameworks, including pilot-stage deployments.
• Experience in regulated financial services or fintech environments is preferred.
• Exposure to model risk management frameworks and regulatory interactions, including SR 11-7.
• Strong proficiency in Python and SQL.
• MLOps experience including Git, GitHub Actions, CI/CD practices, model monitoring, retraining pipelines, and infrastructure automation.
• Hands-on experience with data science platforms such as Databricks, Snowflake, and AWS SageMaker.
• AWS ecosystem expertise including SageMaker, Glue, Lambda, and EventBridge.
• Familiarity with LLM and agentic frameworks, including foundational models (for example, Claude, GPT) and orchestration tools (for example, AWS AgentCore, LangChain).
• Understanding of fraud typologies, AML transaction monitoring methodologies, and detection system design.
• Resourceful and versatile, comfortable operating in a small, fast-moving team environment and delivering with constrained resources.
• Strong technical depth paired with collaborative leadership, guiding teams through ambiguity and turning strategy into actionable delivery.
• Comfort working across Risk, Compliance, and Technology functions.

Tools and technologies

• Python, SQL, Git, GitHub Actions, CI/CD
• Databricks, Snowflake
• AWS SageMaker, Glue, Lambda, EventBridge
• Claude, GPT
• AWS AgentCore, LangChain

Location: Alpharetta, GA (onsite)

Minimum experience: 7 years