AI Product Manager
$120,000–$165,000 year
On-site · New York City, New York, United States
Job Summary
Director-level AI Product Manager in Morgan Stanley's Technology COO organization, focused on GenAI data, reporting, and insights. Responsibilities include defining GenAI data standards and KPIs, producing dashboards to measure adoption, governance, and business impact, identifying trends to inform actions, translating ideas and data into practical product concepts and workflows, and partnering with platform and Infrastructure teams to improve data pipelines and data quality. The role requires hands-on data work, collaboration with multiple stakeholder groups, and a customer-driven approach to deliver scalable AI-enabled solutions.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Business or related discipline
- 3+ years of experience in product management, analytics, data strategy, business intelligence, engineering, or related roles in a large, complex organization
- Experience supporting dashboards, reporting platforms, analytics solutions, or data products used for business decision-making
- Strong analytical skills and data instinct, with the ability to identify patterns, form hypotheses, and turn data into actionable insights
- Ability to translate business priorities into product requirements, data definitions, KPIs, and reporting outputs
- Builder mindset with a strong desire to turn ideas, insights, and hypotheses into practical products, dashboards, prototypes, or workflows
- Customer-driven approach, with the ability to understand stakeholder needs and translate them into useful, scalable solutions
- Strong stakeholder management and communication skills across business and technology teams
- Strong interest in GenAI, AI-enabled product development, and hands-on use of AI tools to improve analysis and execution
- Familiarity with GenAI strategy, adoption measurement, governance, or portfolio reporting (preferred)
- Experience with SQL, Python, Tableau, Power BI, or similar tools (preferred)
- Familiarity with data governance, KPI design, and data quality practices (preferred)
- Experience building prototypes, internal tools, dashboards, workflow solutions, or AI-related products is a plus
- Data science background is a plus
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