What are the responsibilities and job description for the Executive Director-Firmwide AI Data Strategy position at JPMorgan Chase?
The Chief Data Office is responsible for maximizing the value and impact of data globally in a highly governed way. Our teams accelerate the organization's data, analytics, and artificial intelligence journey, spanning data strategy, impact optimization, privacy, governance, transformation, and talent. We are looking for a data executive to help shape the strategy for how we make data available to power everything from new product development to artificial intelligence models. This leader will join the team responsible for setting the enterprise data publishing strategy and driving its adoption across the organization.
As an Executive Director, AI Data Strategy in the Chief Data Office, you will be instrumental in shaping the organization's knowledge representation and data foundations for artificial intelligence. Your role will involve utilizing ontologies and taxonomies to enhance data interoperability and management, preparing enterprise data for AI applications at scale. Your responsibilities will range from engaging with and educating domain experts to assessing standard ontologies and developing an organization-wide ontology framework. Your work will traverse multiple domains, influencing teams across data and analytics, product, and technology.
Job responsibilities
- Drive the development and adoption of the enterprise AI data strategy, aligning stakeholders across business lines and functions.
- Evaluate industry standards and semantic technologies for adoption across the organization, ensuring alignment with strategic objectives.
- Collaborate with stakeholders, subject matter experts, product owners, and engineers to understand use cases, requirements, and dependencies, critically assessing proposed solutions.
- Provide expert input into the enterprise data strategy, with a focus on knowledge representation, ontology design, and data readiness for AI.
- Communicate complex ideas effectively to collaborators and senior leaders using precise terminology and relatable examples, asking clarifying questions to define core meanings.
- Monitor emerging trends and advancements in ontology engineering, knowledge representation, and semantic technologies, translating insights into strategic recommendations.
- Balance timeliness with quality under tight deadlines, managing multiple priorities and cross-functional partnerships.
- Ensure end-to-end relevance to stakeholder needs, from gathering competency questions through to achieving successful integrations.
Required qualifications, capabilities, and skills
- PhD in Artificial Intelligence, Computer Science, Information Science, Knowledge Representation, or a related field.
- 10 years of experience developing enterprise-wide data or AI strategy for large, complex organizations.
- Expertise in data and financial service standards such as ISO 20022, and semantic technologies including OWL, RDF, SKOS, and SHACL.
- Structured thinker and effective communicator with excellent written and verbal communication skills, with the ability to articulate complex technical concepts to senior audiences with poise and confidence.
Preferred qualifications, capabilities, and skills
- Experience applying financial sector ontologies and data standards in production or enterprise environments.
- Experience with program management and collaborative development best practices in large-scale initiatives.
- Understanding of large-scale, distributed, end-to-end data systems and architectures.
- Knowledge of data governance and data management frameworks and their intersection with AI readiness.