Principal Product Manager, AI Data Solutions

Responsibilities

• Define and own the multi-year product strategy for Fact Set's AI Data Mesh.
• Develop a clear point of view on the three primary distribution archetypes and how Fact Set should prioritize and sequence investment across them: inbound data flow via MCP and cloud data shares; interoperability with client-operated AI platforms (Claude, ChatGPT, Microsoft Copilot, and others); and full-push distribution into Snowflake, Databricks, and similar data lakehouse environments.
• Stay at the forefront of the rapidly evolving MCP and agentic AI ecosystem — tracking standards, competitive moves and emerging client deployment patterns.
• Translate strategy into a rigorous, sequenced product roadmap with clear success metrics, technical dependencies, and commercial milestones.
• Work directly with engineering, data architecture, and AI research teams to drive delivery — writing detailed product requirements, participating in architecture reviews, and maintaining accountability for outcomes.
• Define data governance, lineage, and access control frameworks that meet the compliance and security standards of financial services clients.
• Serve as the primary voice of the customer for this product space — conducting regular discovery with client data engineering teams, investment technology leaders, and enterprise architects to validate hypotheses and surface new requirements.
• Partner with Solutions Engineering and GTM teams to define positioning, packaging, and go-to-market motions for each archetype.
• Engage clients at executive level during key sales and partnership discussions.
• Build and maintain an informed perspective on the competitive landscape, with particular attention to moves within financial services and the broader MCP ecosystem.
• Serve as one of Fact Set's internal authorities on MCP and related agentic AI protocols — defining how Fact Set implements, extends, and governs these standards across distribution archetypes.
• Define the technical standards and data contracts that enable reliable, governed AI operation across heterogeneous client environments.
• Evaluate and make build/buy/partner decisions for key platform capabilities in collaboration with architecture and engineering leadership.
• Align stakeholders across product, engineering, data, GTM, and the C-suite on strategy and priorities.
• Mentor other product managers across the organization as this product space grows.

• Bachelor's degree
• 12+ years of professional experience with 8+ years of product management, data engineering, or platform development — with at least 5 years in PM roles owning data infrastructure, AI platform, or developer platform products.
• Proven track record of 0-to-1 product development: taking an ambiguous market opportunity through to a shipped, adopted product, ideally in an enterprise B2B context.
• Experience building products that span technical and business buyers.
• Background at a data infrastructure company, a financial data firm, a major cloud platform, or at a financial AI company.
• Exposure to Financial Services, understanding of how investment managers, banks, and financial institutions think about data governance, compliance, and operational risk.
• Experience engaging C-suite and clients in strategic sales and partnership conversations.
• Working knowledge of AI/LLM deployment patterns — prompt engineering, RAG architectures, tool use, and multi-agent systems.
• Hands‑on familiarity with MCP or comparable data interoperability protocols.
• Comfort with cloud data sharing architectures (Snowflake Secure Data Sharing, Databricks Delta Sharing, or equivalent).
• Ability to engage credibly in technical architecture discussions.
• Experience partnering with GTM and Solutions teams on enterprise sales cycles to help drive product direction.
• An MBA or advanced technical degree is a plus.
• Must be legally authorized to work in the United States without the need for employer sponsorship now or at any time in the future.