Lead Risk Data Scientist & ML Engineer
About The Position
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. 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.
Requirements
- 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)
- 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
Nice To Haves
- Experience in regulated financial services or fintech environments preferred
- Exposure to model risk management frameworks (SR 11-7) and regulatory interactions
- 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
Responsibilities
- 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
- 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
- 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
- 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
- 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
- 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
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
