Senior AI Product Manager - Distribution and Enablement (NYC)

Role Summary

The Sr. Product Manager will own the AI Content Generation roadmap and the delivery across all associated bodies of work. They will be responsible for driving compliant, brand-aligned content at scale, and achieving measurable business impact.  

Responsibilities

• AI Content Generation strategy & roadmap: Translate GD priorities into a clear plan for the “Content Engine” cluster of AI use cases; set outcomes and sequence work across the three named bodies of work to maximize cross-BU reuse (in future horizons) and speed to value. 

• Epic leadership (Content Factory): Lead build-out of the “Content Factory” to convert IPRC thought leadership into persona-tailored, compliant, brand-aligned content. 

• Epic leadership (Investment Commentary): Stand up the investment commentary pipeline by connecting core data sources and generating first-draft outputs with baseline brand/compliance guardrails for selected portfolios; expand per roadmap as readiness allows. 

• Epic leadership (Client Correspondence): Orchestrate the AI agent-driven intake-to-response workflow and ready it for future process expansion.  

• Governance & brand integration: Embed compliance, IP/copyright, and brand-voice libraries into product workflows; ensure output aligns with enterprise standards as capabilities scale.  

• Value tracking & decisions: Partner with GD AI Business Analyst teammates and FP&A to define epic-level success measures up front and use pilot results to recommend scale/pivot/retire, keeping outcomes tied to the established OKRs.  

• Cross-functional orchestration: Coordinate with Tech, Risk/Legal, Marketing, and other key partners to remove blockers and land production-ready releases on plan.  

• Agile Product Ownership: Able to lead one or more squads. Write clear user stories and acceptance criteria, prioritize and maintain the backlog, partner across teams on sprint rituals, align delivery to roadmap outcomes, and validate releases with stakeholders. 

• Business and technical translation: Turn complex AI concepts into plain English for business partners, then convert business needs into clear requirements and acceptance criteria for engineers and data teams. 

• AI and Data Literacy: Create LLM and GenAI use cases for marketing and content. Quickly learn new tools and judge business relevance and risks. 

Qualifications

Required:

• Education and Experience: Bachelor’s degree (or equivalent experience) in business, marketing, computer science, or a related field and 8+ years in product leadership or adjacent roles owning outcomes across multiple teams. 

• Agile Product Ownership: Able to lead one or more squads. Writes clear user stories and acceptance criteria, prioritizes and maintains the backlog, partners across teams on sprint rituals, aligns delivery to roadmap outcomes, and validates releases with stakeholders.

• AI and Data Literacy: Familiar with LLM and GenAI use cases for marketing and content. Quick to learn new tools and judge business relevance and risks.

• AI Application to Business Problems: Ability to identify opportunities to apply AI for process improvement, efficiency gains, or enhanced decision-making.

• Business Needs Translation: Proven ability to turn complex business needs into structured requirements, acceptance criteria, and release plans. 

• Strategic and Analytical Skills: Strong problem solving and prioritization. Comfortable weighing feasibility, cost, risk, and value to sequence work. 

• Governance and Brand Integration: Experience embedding brand voice, IP, privacy, and compliance controls into product workflows and producing required evidence. 

• Vendor and Approvals Navigation: Hands-on experience evaluating third-party tools and coordinating Procurement, Legal, Risk, and Compliance to stay on plan. 

• Measurement and Decisioning: Has set success measures with BAs and other business partners, interprets pilot results, and makes clear scale/pivot/retire recommendations. 

• Communication and Stakeholder Management: Excellent written and verbal communication. Presents options, risks, and recommendations to senior leadership with confidence. 

• Early Adopter Mindset: Passionate about learning AI and modern productivity tools. Resourceful, self-directed, and comfortable building from zero. 

• Ethics and Compliance: Awareness of ethical considerations and compliance when using AI in data analysis (such as bias, data privacy, and transparency).