Risk Management - Data Strategist Lead - Vice President

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.

Job Summary:

As a Data Strategist Lead in Principal Investment Risk Management, you will define and execute the risk data strategy and governance model while partnering across lines of business, functional stakeholders, and technology teams to deliver trusted, decision-grade data products.

You will lead applied AI/Machine Learning delivery-spanning generative AI, agentic workflows, and traditional Machine Learning-to improve risk oversight, analytics, metrics, and reporting.

You will operate with strong ownership from requirements through scaled adoption, ensuring verification, validation, and guardrails that produce safe, reliable outputs in production.

Job Responsibilities:


* Own Principal Risk's data foundations including controlled sourcing/integration, metadata/catalog, lineage, and lifecycle risk controls (protection, retention/destruction, storage, usage, quality).


* Define and evolve data governance standards, publishing patterns, and documentation expectations to enable trusted consumption and self-service.


* Deliver risk data products that support business operations, strategic objectives, analytics, metrics, and reporting across the principal investment process.


* Establish measurable data KPIs (e.g., quality, timeliness, completeness, lineage coverage, control adherence) and use them to steer roadmap and prioritization.


* Design applied AI/ML solutions (generative AI, agentic workflows, traditional ML) to address Principal Risk analytics and oversight use cases.


* Implement LLM agents and multi-agent systems including planning, parallel task execution, entity resolution, and human-in-the-loop escalation.


* Translate business needs into delivery artifacts including technical designs, acceptance criteria, measurable outcomes, and execution plans.


* Lead end-to-end delivery from requirements → POC → production → scaled adoption, including operating model handoff where needed.


* Build verification and validation mechanisms such as business-rule checks, evaluation datasets, and regression testing to ensure safe and reliable outputs.


* Operate production solutions through monitoring, performance/stability improvements, drift management, and continuous iteration.


* Partner with technology teams to document data sources, formats, and flows while implementing validation to ensure downstream readiness for analytics and reporting.

Required Qualifications, Capabilities, and Ski...