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.

Job Responsibilities

• Define a multiyear 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 nontechnical audience
• Convene and lead decision forums across Product Data Science Engineering Architecture and others to agree standards and exceptions.
• Navigate competing priorities broker tradeoffs and drive outcomeoriented 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 predeployment gates and postdeployment monitors.
• Pilot and institutionalize progressive delivery (canary/bluegreen) shadow testing rollback protocols and automated drift/decay detection.
• Develop crisp executiveready value stories backed by KPIs (e.g. cycletime 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 businesscasebacked proofsofconcept and lead adoption where ROI is clear.

Required qualifications capabilities and skills

• Bachelors 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 handson 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).
• Executivelevel communication and storytelling skillsable 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.

Preferred qualifications capabilities and skills

• Advanced degree preferred.
• Experience implementing progressive delivery for ML (canary bluegreen) 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.