Group Vice President, Product Management

Job Title

Group Vice President, Product Management – Head of Product (Data Analytics & AI)

Hoboken (HQ), NJ, USA

• Unified Product Strategy &
  
  Roadmap:
  
  Own an integrated strategy and roadmap across AI & Data Analytics Platform Services, Database Solutions, and Audience Solutions, ensuring the product lines share a common foundation and build on shared capabilities over time.
• Commercial Requirements Translation:
  Own commercial intake, evaluate priorities against strategy, and convert them into a sequenced roadmap with milestones, business cases, and clear launch/hold decisions.
• Product Architecture:
  Design the product architecture to serve corporate buyer demand and near‑term revenue opportunities.
  • Define and sequence Applied Research Intelligence Platform capabilities, including structured extraction and enrichment, knowledge graph construction, enterprise APIs, Model Context Protocol (MCP) access, rights and entitlements governance, and clean‑room execution (privacy‑safe analysis environments).
  • Lead the productization of Wiley's proprietary scientific collections by transforming legacy‑format content into structured, machine‑readable, high‑margin data products.
  • Define the product vision for advertising and audience engagement, supported by behavioral analytics that turn Wiley's opted‑in first‑party audience into sellable insights and signals to inform structured data extraction priorities.
• Market Validation & Evidence‑Based Prioritization:
  Define leading indicators for execution and partner with data science and commercial teams to validate the content‑depth advantage use case by use case, investing where depth is proven.
• Build/Buy/Partner Decisions:
  Lead the product dimension of the build/buy/partner agenda by defining the commercial framing, strategic rationale, and partner guardrails.
• Global Team Management:
  Lead and develop a high‑performing, multi‑disciplinary product organization spanning product management, UX, and product strategy, and build the processes and culture required for software‑led, enterprise‑grade execution.
• Strategic
  
  Collaboration:
  
  Partner with the GM, commercial leaders, Head of Data Science, and Head of Technology to ensure the product strategy and roadmap realize the division's AI‑powered vision.

• Enterprise Product Leadership: 15+ years of product management experience, with at least 7 years leading product organizations in enterprise SaaS, data products, or AI/ML platforms. Demonstrated track record of owning a product portfolio from early‑stage validation through scaled commercial deployment including revenue achievements, pricing architecture, access models, and product economics.
• Commercial Strategy & Positioning:
  Ability to translate complex technical features into clear, outcome‑based value propositions for different buyer personas (e.g., C‑Suite vs. End User). Experience in designing and iterating SaaS pricing models (usage‑based, seat‑based, or tiered) to maximize Net Revenue Retention (NRR).
• Data & AI Product Depth:
  Proven experience building and commercializing data products (structured datasets, APIs, knowledge graphs, AI‑powered analytics) for enterprise buyers in regulated markets. Understanding of provenance, rights governance, and compliance (GDPR, CCPA) as product requirements.
• Corporate R&D Market
  
  Experience:
  
  Familiarity with corporate R&D buyers in pharma, biotech, engineering, or adjacent verticals – including their procurement cycles, integration expectations, and competitive landscape.
• Platform Thinking:
  Demonstrated ability to run a multi‑product portfolio on a shared platform foundation, avoiding one‑off solutions that are costly to maintain and don't create long‑term customer or revenue value.
• Commercial Instinct:
  Strong grasp of product economics (usage‑based pricing, tiered subscriptions, API monetization, data licensing, access controls, and usage tracking) and ability to tie product decisions to ARR, NRR, and unit economics.
• Interpersonal & Organizational Complexity:
  Proven ability to lead through shared accountability across commercial, data science, and engineering stakeholders.
• Technical Literacy:
  …