Lead Risk Data Scientist & ML Engineer

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

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

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
• 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
• 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