Lead Risk Data Scientist & ML Engineer

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 expertise: SageMaker, Glue, Lambda, EventBridge, and related services
Familiarity with LLM and agentic frameworks: foundational models (Claude, GPT, etc.), agent orchestration tools (AWS AgentCore, LangChain, etc.)
Understanding of fraud typologies, AML transaction monitoring methodologies, and detection system design
Leadership Profile
Resourceful and versatile: thrives in a small, fast-moving team; comfortable wearing multiple hats and delivering with constrained resources
Startup mentality: pragmatic problem-solver who ships solutions; bias toward execution and measurable outcomes
Combines technical depth with collaborative leadership. Guides project teams through ambiguous problems and drives clarity, structure, and delivery
Collaborates effectively across Risk, Compliance, and Technology functions; comfortable operating in ambiguity and translating strategy into action