Risk Management - Data Strategist Lead - Vice President

JPMorgan Chase & Co.•New York, NY

27d•$100,000 - $198,000

About The Position

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.

Requirements

Bachelor’s degree (or equivalent experience) in a relevant field (e.g., data science, computer science, engineering, math, sciences) or equivalent professional experience.
5+ years of experience in data management, data governance, risk management/analytics, data science, or a closely related domain.
Demonstrate strong analytical problem-solving skills with the ability to execute effectively in time-sensitive environments.
Communicate clearly in writing and verbally, producing high-quality documentation and influencing business, risk, and technology stakeholders.
Apply foundational knowledge of data management principles and end-to-end data lifecycle management.
Program effectively in Python and work confidently with SQL (or similar querying languages).
Build and interpret dashboards/analysis using Tableau (or equivalent BI experience aligned to the role’s needs).
Collaborate cross-functionally to define requirements, align stakeholders, and drive approvals for delivery and adoption.
Execute delivery with accountability, including defining outcomes, tracking progress, and managing dependencies through to production.
Promote strong data quality and control discipline, including validation and readiness for downstream reporting and analytics.
Adhere to data protection and usage expectations appropriate for risk data and decisioning workflows.

Nice To Haves

Deliver hands-on experience with an LLM platform (model onboarding/serving, prompt/version management, evaluations).
Build experience with agent orchestration frameworks (tool use, retrieval-augmented generation, state/context management).
Operationalize production systems with observability, alerting, dashboards, runbooks, and post-deploy monitoring/continuous improvement.
Use familiarity with data governance tooling for catalog/metadata/lineage and modern data publishing standards.
Leverage familiarity with big data platforms, data architecture patterns, and governance tools/platforms.
Apply cloud/DevOps exposure (e.g., AWS, Linux, Git) and observability tooling experience to support reliable delivery.
Demonstrate a track record of measurable improvements through automation, operational rigor, and end-to-end data lifecycle initiatives (onboarding, integrity checks, archiving, migration/decommissioning).

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.