VP, Data Trust & AI Readiness

Data is at the heart of how Chase drives innovation and competitive advantage. Within Consumer & Community Banking, the Data & Analytics organization empowers our bankers, clients, and executives to make smarter decisions — and that power depends on data that is trustworthy, well-described, and usable at scale. As the program team, we set the standards, shape the roadmap, and prove what's possible through rapid prototypes, partnering with the data and engineering teams who build and own the capabilities that get us there.

As a Vice President on the Data Modernization team, you will lead the work to make CCB's structured and unstructured data discoverable, interpretable, and dependable — and ready for the next generation of analytics and AI. You will define the metadata standards and data domain patterns — business, technical, and operational — that make data easier to find, understand, and trust. As a hands-on leader, you will influence data owners, engineers, and senior stakeholders to raise data quality, enrich metadata, and prepare trustworthy data for downstream analytics, conversational querying, and GenAI experiences. Above all, you will be a force multiplier — turning ambiguity into standards, one-off fixes into scalable patterns, and technical progress into clear executive narratives.

Job Responsibilities: 

• Shape and drive adoption of the enterprise data readiness framework across CCB business units.
• Define and champion standards for business, technical, and operational metadata so data is well-defined, discoverable, and trustworthy at scale.
• Establish semantic and context standards that improve the consistency, interpretability, and reuse of data across CCB analytics and AI systems.
• Lead profiling of priority domains to surface definitional, lineage, and data-quality gaps, and partner with data owners to close them.
• Convert one-off fixes into repeatable, scalable enrichment patterns — and mentor others to apply them.
• Advise data leaders and engineers on the quality and usability improvements that create the most value across large datasets.
• Build and showcase prototypes that demonstrate improved data readiness for analytics and AI-assisted use cases, including conversational and agentic experiences.
• Own readiness scorecards and KPIs, translating progress into clear inputs for maturity assessments, roadmap decisions, and executive updates.

Required Qualifications, Capabilities, and Skills 

• Bachelor's degree in a quantitative, scientific, or technical field (e.g., Mathematics, Statistics, Computer Science, Engineering, Economics, or a related discipline), or equivalent practical experience.
• 7 years of relevant experience in data science, data management, data governance, data quality, or analytics engineering, including a track record of setting standards and influencing across teams.
• Deep knowledge of metadata management and data catalog tools, with an emphasis on discoverability, lineage, and interpretability.
• Hands-on experience with structured and unstructured data at scale — profiling, cleansing, standardizing, and documenting large datasets on enterprise data platforms or data products.
• Strong command of data quality frameworks and the ability to diagnose, measure, and drive remediation of quality issues.
• Understanding of ontology, semantic and context layers, and how consistent definitions improve reuse across analytics and AI systems.
• Solid SQL and analytical problem-solving skills, including root-cause investigation across large data volumes.
• A first-principles mindset — questioning assumptions and ensuring data makes sense in context, not just in the aggregate.
• Awareness of how conversational analytics, natural-language querying, and agentic AI consume data, and the data conditions they depend on.
• Strong attention to detail and an uncompromising commitment to accuracy.
• Proven experience collaborating across product, engineering, and business teams in a regulated environment.
• Clear written and verbal communication, including preparing and presenting updates for senior stakeholders.

Preferred Qualifications, Capabilities, and Skills 

• Experience in consumer banking or another large-scale, high-volume data environment.
• Exposure to building or governing semantic models and metrics layers for enterprise analytics.
• Familiarity with data domain modeling and standardization across multiple business units.
• Scripting or full-stack skills (e.g., Python) that support data profiling, enrichment, and rapid prototyping.
• Applied exposure to AI/ML and GenAI concepts from the perspective of a consumer of well-governed data.