Transform Consumer and Community Banking (CCB) Operations oversight with AI-enabled, full-population controls testing and audit-ready evidence. You’ll own end-to-end analytics and Large Language Model (LLM) enabled automation for exception triage and reporting, while governing an enterprise knowledge base that stays current, trusted, and regulator-aligned.
As a Continuous Monitoring Quality Manager on the Quality team, you will partner closely with CCB Operations business lines (e.g., Collections/Recovery/Auto Operations) to design, develop, and implement AI-enabled continuous monitoring and full-population quality testing frameworks that strengthen the control environment, improve audit readiness, and drive operational excellence at scale. You will own end-to-end delivery of data analytics solutions, test population isolation, test design, controls reporting, and LLM-enabled automation (including prompt engineering, workflow integration, and governance). You will also serve as a strategic owner for an enterprise knowledge base, ensuring standards, content governance, and alignment with operational and regulatory expectations.
Job responsibilities
- Deliver AI-enabled full-population monitoring, shifting oversight from sampling-based review to automated controls testing and scalable evidence capture.
- Design and implement end-to-end continuous monitoring tests by isolating test populations, defining business rules/thresholds, implementing automated validation and reconciliation checks, partnering with operations, compliance, risk, and control stakeholders to ensure coverage and intent alignment.
- Drive prompt engineering and LLM workflow delivery to accelerate knowledge base curation and standardization, controls reporting narratives and executive summaries (grounded in approved sources), intelligent triage/routing of exceptions (with human-in-the-loop controls where required).
- Establish LLM/prompt governance practices including versioning, performance testing, outcome validation, documentation, and controlled change management suitable for a regulated environment.
- Own the enterprise knowledge base strategy, including taxonomy/metadata standards, content lifecycle (create/review/retire), and quality controls to ensure content remains current, trusted, and auditable.
- Proactively analyze data to identify themes, trends, and performance opportunities; translate insights into prioritized improvements and automation roadmap items.
- Prepare and deliver management reporting on test results, key performance metrics, emerging risks, and remediation plans—clearly communicating impacts and actions.
- Demonstrate urgency responding to escalations, adverse performance indicators, and shifting priorities to maintain program effectiveness.
- Handle highly confidential information with professionalism and integrity, adhering to JP Morgan Chase privacy and security standards.
Required qualifications, skills, and capabilities
- Seasoned experience in data analytics/development with a focus on quality testing methodologies, controls testing, or continuous monitoring.
- Seasoned experience delivering automation at scale (test frameworks, exception management, reporting automation, repeatable controls reporting).
- Working knowledge of AI-enabled delivery concepts, including prompt engineering (structured outputs, reusable templates, test cases), workflow controls (human-in-the-loop, approvals), validation approaches to ensure accuracy/consistency of automated outputs.
- Advanced skills in Microsoft Office products (Excel, PowerPoint, Access) for complex analysis and executive-ready communication.
- Excellent written/verbal communication skills to translate technical findings into clear recommendations for diverse stakeholders.
- Proven ability to deliver results in high-pressure environments with tight deadlines and shifting priorities.
- Strong problem-solving and conflict resolution skills.
Preferred qualifications, skills, and capabilities
- Demonstrated experience supporting Collections/Recovery/Auto Operations environments and related datasets.
- Proven experience with enterprise knowledge management (taxonomy, metadata, governance, content lifecycle).
- Strong familiarity with AI enablement patterns such as grounding/RAG-style approaches (ensuring outputs align to approved knowledge sources) and evaluation methods to monitor quality over time.
- Hands-on experience with Webstats and/or ACES.
- Relevant certification in Six Sigma, Lean, or similar process improvement methodologies.
- Influential ability to drive change across business, technology, and control stakeholders to embed quality practices.