Product Manager

NBCUniversal•North Hollywood, NY

1d•Hybrid

About The Position

The Product Manager, Enterprise Data & AI will join NBCUniversal’s Enterprise Data + AI Product organization and help turn business problems into products, solutions, and experiences that create measurable value across the company. This hands-on product-building role sits at the intersection of business, product, data, and AI. The Product Manager works directly with business and product teams to understand workflows, uncover problems and opportunities, connect needs to enterprise capabilities, and move ideas from discovery through validation and adoption. The role combines product judgment, business curiosity, technical fluency, and a bias toward action. It calls for someone comfortable engaging business stakeholders, understanding data and AI capabilities, shaping use cases, building or prototyping solutions with modern tools, communicating product value, and assessing whether experiences deliver measurable results. The position emphasizes working through ambiguity and using technology—particularly AI—to accelerate progress from an idea to a business outcome, rather than focusing solely on managing a feature backlog. The position currently follows a hybrid schedule, requiring work from the office at least four days per week. NBCUniversal may change in-office requirements. The position is eligible for company-sponsored benefits.

Requirements

Bachelor’s degree or equivalent practical experience.
At least 5 years of experience in Product Management, Product Strategy, technical Product Marketing, MarTech/AdTech, Solutions, Data/AI products, customer-facing technology, or a related product and technology discipline.
Demonstrated ability to take an ambiguous customer or business problem from discovery through a clear product opportunity, solution, or measurable outcome.
Strong product judgment and customer-discovery skills, including the ability to challenge a stated request and uncover the underlying business need.
Strong data fluency and comfort working with analytics, metrics, data products, and technical concepts.
Hands-on experience using AI-assisted development, prototyping, or automation tools to create working prototypes, workflows, or other tangible solutions.
Ability to translate complex technical concepts into clear, compelling, actionable guidance for technical and non-technical audiences.
Strong stakeholder-management and cross-functional leadership skills, with the ability to influence without formal authority.
Ability to work independently, create structure from ambiguity, and drive work from problem through outcome with minimal oversight.
Excellent written, verbal, and visual communication skills.

Nice To Haves

Experience with enterprise data platforms, MarTech, AdTech, identity, CRM, analytics, AI platforms, or other data-intensive product environments.
Experience with GitHub Copilot; experience with Claude Code, Cursor, or similar AI development environments is also valuable.
Experience building prototypes, lightweight applications, automations, or agentic workflows without relying exclusively on an Engineering team.
Experience developing product positioning, go-to-market strategies, demonstrations, enablement, or adoption programs for technical products.
Experience establishing Voice of the Customer, customer-success, product-usage, or adoption feedback loops.
Familiarity with GitHub, Jira, Confluence, Figma, and analytics or BI platforms.
Builder mentality, with a bias toward experimentation, learning, and tangible outcomes over process for its own sake.
Intellectual curiosity and willingness to rapidly learn unfamiliar business and technical domains.
Strong ownership and follow-through; proactive approach to identifying gaps and moving work forward without waiting for perfect clarity.
Ability to move between strategy and execution, from senior-level business conversations to hands-on building and problem solving.

Responsibilities

Build trusted relationships with enterprise stakeholders, understand their workflows, and proactively identify problems, opportunities, and unmet needs.
Translate stakeholder needs into clear use cases, product opportunities, hypotheses, and measurable outcomes; connect opportunities to existing enterprise products and capabilities where possible.
Use AI-assisted development, prototyping, and workflow tools to rapidly create tangible ideas, test assumptions, and accelerate learning and decision-making.
Develop product positioning, demonstrations, enablement materials, and engagement motions to support understanding, adoption, and successful use of enterprise products and capabilities.
Establish feedback loops using stakeholder input, qualitative insights, usage signals, and business outcomes to identify friction, unmet needs, and opportunities for improvement.
Define and track measures of adoption, engagement, and business value; use quantitative and qualitative data to inform decisions and communicate results.
Share business, customer, and adoption insights with Product Managers and product leaders to inform roadmap priorities, investment decisions, and portfolio direction.
Partner across Product Management, Product Operations, Engineering, Data, AI, and business teams to move opportunities toward scalable solutions without relying on formal authority.
Make complex data, technology, and AI capabilities understandable through product narratives, demonstrations, executive-ready materials, and reusable enablement assets.
Apply AI to discovery, research, analysis, prototyping, documentation, and workflow automation, and identify ways to increase organizational leverage and self-service.