Risk Management - Consumer and Community Banking Risk Product - Innovation Product Manager - Executive Director

Bring your Expertise to JPMorgan Chase. As part of Risk Management and Compliance, you are at the center of keeping JPMorgan Chase strong and resilient. You help the firm grow its business in a responsible way by anticipating new and emerging risks, and using your expert judgement to solve real-world challenges that impact our company, customers and communities. Our culture in Risk Management and Compliance is all about thinking outside the box, challenging the status quo and striving to be best-in-class.

As the Executive Director within Risk Management and Compliance, you will exercise broad influence and lead cross-functionally across Product, Data Science, Architecture, Technology (Engineering, Machine Learning Operations), Security, and Risk/Model Governance. This role will also be responsible for promoting the innovation agenda across Consumer and Community Banking Risk Products, ensuring acceleration of key capabilities are delivered at scale and services are reusable across all critical Product and Risk Analytics functions.

• Define a multi‑year innovation strategy based on existing plans and goals for model delivery and end-to-end risk products that balances speed, risk and controls, and cost efficiency; establish North Star metrics and an execution roadmap.
• Translate strategy into funded initiatives with clear KPIs and value narratives for executive and non‑technical audience
• Convene and lead decision forums across Product, Data Science, Engineering, Architecture and others to agree standards and exceptions.
• Navigate competing priorities, broker trade‑offs, and drive outcome‑oriented alignment.
• Understand the voice of our clients across the CCB Risk organization and diagnose bottlenecks from research handoff through production; redesign processes and automate approvals, testing, and promotion steps.
• Establish enterprise "golden paths" for packaging, validation, and deployment across dev/test/prod with integrated model registries, feature stores, and approval workflows.
• Embed model risk controls, documentation, and auditability into pipelines and artifacts to satisfy regulatory requirements without slowing delivery.
• Standardize reusable controls for fairness, explainability, performance, and data quality, including pre‑deployment gates and post‑deployment monitors.
• Pilot and institutionalize progressive delivery (canary/blue‑green), shadow testing, rollback protocols, and automated drift/decay detection.
• Develop crisp, executive‑ready value stories backed by KPIs (e.g., cycle‑time reduction, automation coverage, change failure rate, MTTR); report quarterly to senior stakeholders.
• Tailor communications for engineers, product managers, risk partners, and business leaders; run roadshows, office hours, and targeted training and evaluate emerging MLOps and GenAI delivery practices and vendors; run business‑case‑backed proofs‑of‑concept and lead adoption where ROI is clear.

• Bachelor's degree in Computer Science, Engineering, Mathematics, or related field
• 10+ years across Data Science, MLOps, Software/Platform Engineering, or related disciplines, including 5+ years delivering production ML at scale.
• Deep hands‑on familiarity with modern MLOps and platform practices:
  • CI/CD and infrastructure as code (e.g., GitHub Actions/GitLab/Jenkins; Terraform/CloudFormation)
  • Containerization/orchestration (Docker, Kubernetes) and service deployment patterns
  • Model registries and feature stores; lineage and metadata management.
  • Automated testing for ML (data quality, unit/integration, performance, bias/fairness, explainability).
  • Observability for data and models (drift, performance SLOs, alerting, rollback).
• Executive‑level communication and storytelling skills-able to synthesize complex technical change into business outcomes and influence senior leaders.
• Systems thinking and process design mastery; comfortable mapping current vs. target state and executing iterative change with measurable KPIs.
• Cloud platform experience for ML services and batch scoring; cost management for ML workloads.

• Advanced degree preferred.
• Experience implementing progressive delivery for ML (canary, blue‑green) and shadow/AB testing at scale.
• Background partnering with audit, compliance, and legal on model documentation and attestations.
• Exposure to GenAI and evaluation frameworks for safety, quality, and performance.
• Exposure to Databricks.
• Strong understanding of risk management and governance in regulated environments, with a strong knowledge of modeling in particular; ability to design "controls as code" and streamline approvals.