AI & Data Analytics Business Unit
Position Overview
Applied Research Intelligence is positioned to become a category-defining business that connects the world’s scientific knowledge with the decisions it can inform. As Group Vice President and General Manager, Applied Research Intelligence, you will be the commercial owner and business builder at the center of this opportunity, leading a high-growth business focused on enterprise R&D intelligence.
You will report to the Chief AI & Data Analytics Officer and lead a high-performing commercial organization focused on turning proprietary scientific content, data assets, and AI-enabled capabilities into scalable solutions for enterprise customers across pharmaceuticals, biotechnology, engineering, agriculture, and adjacent innovation-driven sectors.
This is a build mandate: you will have full profit-and-loss ownership, the authority to shape the commercial model, and the opportunity to scale a business that sits at the intersection of scientific expertise, proprietary data, and applied AI.
You will be the visible, commercially accountable leader customers, cloud and technology partners, and strategic co-development partners engage with when they want to understand the business’s direction, ambition, and ability to win in a rapidly evolving market.
Key Responsibilities
P&L Ownership
- Hold full profit-and-loss accountability for a consolidated Applied Research Intelligence business of approximately $100M+ in revenue, spanning content knowledge feeds, database solutions, commercial licensing and partnerships, and the newly launched Applied Research Intelligence platform.
- Own the revenue forecast, financial performance, and operating rhythm for all four business lines, using commercial levers to manage variance, improve predictability, and close gaps against plan.
- Lead the shift toward higher-quality recurring revenue, increasing the recurring mix from 80% through platform subscriptions, data-feed contracts, database SaaS migration, and recurring partnership arrangements.
- Own the operating expense budget and multi-year investment plan across technology, product, sales, marketing, and operations, ensuring capital is deployed against clear milestones and measurable value creation.
- Own pricing strategy and models across all four business lines.
- Govern the business's dependency on shared AI & Data Analytics platform infrastructure, including product, engineering, and data science capabilities that sit outside this role's direct organization, ensuring commercial commitments and revenue targets are matched by platform delivery capacity, and escalating capacity or roadmap misalignment before it puts revenue commitments at risk.
- Exercise strong influence over the shared product/engineering roadmap, ensuring platform delivery capacity is matched to commercial commitments.
Commercial Ownership
- Define the market and commercial strategy, customer engagement model, pricing approach, and platform readiness required to scale the business: deciding where to compete, building the commercial infrastructure to win, and converting data, content, and AI-enabled products into durable revenue.
- Build a commercial team and culture suited to a business in transition: hire and develop leaders who are entrepreneurial, customer-led, disciplined on execution, and comfortable moving from legacy content models toward scalable intelligence platforms.
Corporate R&D Go-to-Market
- Lead enterprise go-to-market across the three target buyer archetypes: top-tier pharma AI and data science teams; corporate R&D; and librarians and knowledge managers.
- Drive demand generation and account-based marketing programs with a clear pipeline handoff rhythm that connects marketing investment directly to commercial outcomes.
- Develop a repeatable commercial playbook for life sciences and healthcare that can be extended into other enterprise R&D verticals.
Strategic Partnerships & Competitive Positioning
- Own the commercial relationship and deal structure for strategic partnerships across product extensions, vertical AI applications, commercial licensing, and partner-led distribution models.
- Build integration and referral partnerships across clinical decision support, pre-clinical drug discovery, personalized medicine, education, and certification use cases.
- Own and evolve the IP and content-licensing strategy, including structuring multi-party licensing agreements and optimizing licensing yield across content and data assets.
Market Intelligence & Demand Signal
- Serve as the primary source of enterprise R&D market intelligence for the business: translating customer conversations, partner input, evidence from the market, and competitive observations into actionable demand signals for product and data science leaders.
- Identify which use cases are active in customer conversations, which evidence and content-depth questions enterprise buyers are asking, and which competitive capabilities are shaping buying decisions.
- Contribute to vertical selection and use-case prioritization by providing clear commercial input on market attractiveness, buyer urgency, differentiation, and invest-or-do-not-invest decisions.
- Maintain awareness of adjacent customer segments and cross-sell opportunities where enterprise R&D, knowledge management, and audience-based buyer needs overlap, including adjacent data categories such as clinical/regulatory and other domain data relevant to enterprise R&D buyers.
Required Experience & Qualifications
- Experience leading commercial or product organizations within scientific, patent/IP, or regulated R&D data ecosystems (e.g., pharma, biotech, patent analytics)
- Proven track record as a commercial GM with full P&L ownership in a data, AI, or technology business.
- Experience building, scaling, and commercializing complex data, AI, knowledge/ database, or technology products for enterprise buyers, including managing investment decisions against clear milestones and performance indicators.
- Deep enterprise go-to-market experience in corporate R&D markets such as pharmaceuticals, biotechnology, engineering, agriculture, or adjacent sectors; demonstrated ability to navigate technical buying processes, build trust with AI and data science teams, and close multi-year enterprise contracts.
- Strong commercial fluency in data product economics, including licensing models, subscription architectures, usage-based pricing, API monetization, and the shift from transactional revenue toward recurring, subscription-based income.
- Demonstrated ability to build and lead high-performing commercial organizations through a period of significant strategic transformation while maintaining performance in existing revenue lines.
- Track record of building strategic partnerships that generate near-term commercial return.
- The commercial credibility and technical fluency to represent the business externally to enterprise buyers, cloud and technology partners, investment committees, and the board.
- A builder’s orientation: energized by creating new markets, shaping teams and operating models, testing commercial hypotheses, and scaling from early traction to repeatable growth.
- First-hand experience inside a corporate R&D, IP, or scientific operations function is a strong plus.
- Advanced degree (MBA, PhD, or equivalent) in a scientific or business discipline preferred.
Success Metrics
- Deliver 12% year-over-year consolidated revenue growth while continuing to expand recurring revenue by at least 10%.
- Shift the revenue mix so platform subscriptions, database API/SaaS offerings, and recurring licensing arrangements exceed one-time training revenue.
