What are the responsibilities and job description for the Data Science Lead - Vice President position at JPMorgan Chase?
Shape the future of data science while crafting solutions that enhance and optimize customer experiences. Lead end-to-end processes, manage dependencies, and liaise with stakeholders as part of a team at the forefront of innovation.
As a Data Science Vice President within the Commercial and Investment Bank Technology team, you are an integral part of a team that works to develop high-quality architecture solutions for various software applications and platforms products. You drive significant business impact and help shape the target state architecture through your capabilities in multiple architecture domains.
Job responsibilities
Engage with stakeholders to identify and define business needs and opportunities, and design technical solutions to address them.
Partner with product managers, engineers, and functional experts across the end-to-end AI/ML lifecycle—conception, validation, scaling, production delivery, and performance monitoring.
Own end-to-end delivery of the Metrics Store (MORPHEUS) program—defining roadmap, managing dependencies, and tracking milestones across data engineering, semantic layer, and BI integration workstreams.
Facilitate cross-functional workshops with domain experts in Payments Liquidity, Merchant Services, and Embedded Banking to elicit, validate, and govern metric definitions.
Drive adoption of the unified semantic layer by crafting clear value narratives for managing directors and operational stakeholders, and removing organizational blockers to SME participation.
Apply lean and agile delivery practices—running structured sprint ceremonies, maintaining a prioritized backlog, and iterating rapidly on platform capabilities based on stakeholder feedback.
Develop deep subject-matter expertise in Payments processing, transaction lifecycle, and operational SLOs to translate business intent into precise metric specifications.
Required qualifications, capabilities, and skills
Advanced degree (master’s or higher) in Computer Science, Data Science, Engineering, Applied Mathematics, Statistics, or a related quantitative discipline.
5 years of experience developing and deploying Data Science solutions.
3 years of experience as a Technical Program Manager or equivalent role delivering data platform, analytics, or metrics infrastructure programs in a regulated financial services environment.
Hands-on proficiency with modern data stack components—dimensional modeling, dbt or equivalent transformation frameworks, and columnar/lakehouse storage—sufficient to independently evaluate technical trade-offs.
Demonstrated ability to define and socialize a product vision, build consensus with skeptical domain experts, and translate ambiguous business requirements into actionable engineering specifications.
Experience operating in lean/agile environments with rapid iteration cycles; comfort with ambiguity and willingness to own outcomes end-to-end without a fully staffed team.
Strong data literacy: ability to read and reason about SQL, understand metric grain and aggregation correctness, and identify when a proposed definition will produce inconsistent results across contexts.
Preferred qualifications, capabilities, and skills
Practical experience with semantic layer technologies (dbt MetricFlow, LookML, AtScale, or equivalent) and an appreciation for the governance challenges of a shared metrics store across competing business domains.
Background in payments, card processing, or transaction banking
Exposure to federated query engines, data observability tooling, or real-time monitoring applications