СШАStrategy & Solutions Lead
5 дн. назад
180k–240k USD / годСШАLeadHybrid
Lead strategy and solutions to incubate and validate new growth opportunities at the intersection of AI, data, product, and pharma/biotech customers.
О компании
- is redefining how humans acquire knowledge and develop insights in biomedicine. Our AI-powered platform enables researchers and decision-makers to discover and interpret evidence from millions of scientific publications, clinical trials, regulatory documents, and other complex data sources in minutes.
- We are building the world’s most advanced biomedical knowledge platform, powered by a high-precision Knowledge Graph and GenAI capabilities. Our technology is already used by leading biopharmaceutical organizations to accelerate drug discovery, improve safety, and drive better decision-making.
- Backed by top-tier investors including ICONIQ, Index Ventures, Pentech, and Marathon, we are scaling rapidly and expanding our product suite and market presence.
Обязанности
- is hiring a Strategy and Solutions Lead to incubate and validate new growth opportunities at the intersection of AI, data, product, and our pharma and biotech customers across R&D and commercial functions.
- This role focuses exclusively on new territory — new capabilities, solution lines, integrations, or market segments — where customer demand goes beyond what our product supports out of the box. The mandate is to turn ambiguous enterprise demand into scalable, repeatable solution plays grounded in real pharma workflows, drive executive decision-making, and then hand those plays into BAU teams once validated.
- You will work directly with ’s executive leadership and lead discussions with senior stakeholders across pharma R&D and commercial functions, as well as their data and technology teams, to shape strategic partnerships, solution architecture, and product decisions. You will operate with significant autonomy, owning opportunities from the first strategic conversation through to executive decision and handoff.
Требования
- ◆ 7+ years’ experience at the intersection of pharma or life sciences strategy, AI and data, and enterprise customers, with a track record of leading discussions with senior R&D and commercial stakeholders and shaping ambiguous asks into scalable solutions.
- ◆ Strong fit from one of the following backgrounds:
- ◇ Life science strategy or AI consulting at Engagement Manager / Associate Partner level, with substantial R&D or commercial work and ownership of AI or data programmes from strategy through to scale-up
- ◇ Enterprise Solutions / Solutions Strategy roles in B2B SaaS serving pharma and biotech, particularly in AI-, data-, or platform-heavy products
- ◆ Deep understanding of end-to-end pharma R&D and commercial workflows and the decisions within them (e.g. target identification, Target Product Profile, candidate selection, indication expansion, launch and lifecycle planning), and of how data and AI can change them.
- ◆ Hands-on experience building, deploying, and/or integrating technology solutions in enterprise settings (e.g. data platforms, AI and LLM applications, API and system integrations). You have been close enough to the build to understand how a solution works, not just what it does, and have seen solutions through to production.
- ◆ Ability to read and challenge solution architectures, reason through data pipelines, APIs, and integration patterns, and understand how AI and LLM systems are built and evaluated (e.g. retrieval-augmented generation, agentic workflows, knowledge graphs).
- ◆ Experience turning complex, bespoke-seeming enterprise demand into repeatable playbooks (qualification criteria, scoping frameworks, pilot design, and BAU handoff).
- ◆ Strong judgement in making and defending pursue / park / decline recommendations based on product direction, feasibility, scalability, and business impact.
- ◆ Comfort driving executive decisioning (internally and on the customer side) across Product, Engineering, and GTM, and translating technical trade-offs into clear business choices.
Будет плюсом
- ◆ Scientific or quantitative background (e.g. advanced degree in life sciences, engineering, or a related field).
- ◆ Experience on AI or GenAI programmes in pharma, from use-case definition through to scaled deployment.
- ◆ Prior experience in a technical capacity (e.g. forward deployed engineer, data scientist, software engineer, technical product manager).
- ◆ Familiarity with enterprise data governance in regulated environments (e.g. GxP, data privacy, information security reviews).
- ◆ Exposure to market expansion, new segment GTM, technology partnerships, or solution incubation.
Другое
- ◆ Own intake and shaping of strategic enterprise opportunities that require new capabilities, integrations, packaging, or market positioning.
- ◆ Drive executive decisioning on whether to pursue, park, or decline opportunities, with clear rationale, options, and business and technical implications.
- ◆ Maintain a clear boundary between incubation work and BAU; ensure this team is only engaged where something new must be created.
- Lead senior R&D and commercial conversations and translate demand into scalable solution paths
- ◆ Lead high-stakes discussions with senior stakeholders across pharma R&D (e.g. Discovery, Translational Science, Clinical Development) and commercial functions (e.g. Portfolio Strategy, New Product Planning, Medical Affairs, Market Access).
- ◆ Ground discovery in customers’ end-to-end workflows and decision points (e.g. target identification and validation, Target Product Profile development, candidate selection, indication expansion, lifecycle management) to define target state, success criteria, and constraints.
- ◆ Translate ambiguous asks into a solution concept and delivery path, including where creates value in the workflow, scope boundaries, sequencing, timelines, risks, dependencies, and mutual commitments.
- ◆ Shape expectations and commercial framing to ensure scalable commitments (what is standard, what is configurable, what requires investment, and what is out of scope).
- ◆ Work with customer architecture, data, IT, and AI teams in working-level depth on ’s platform capabilities, APIs, data model, and integration patterns (e.g. with internal data lakes, knowledge management systems, and enterprise AI tooling), going beyond high-level positioning to a solution design both sides can build against.
- ◆ Drive the solution design for new plays, including integration approach, data flows, security, and deployment model, and work alongside ’s Engineering and Data teams through build, testing, and deployment, without being the engineer writing production code.
- ◆ Pressure-test technical feasibility early: review architectures, spot integration and data risks, and make informed calls on what can be configured, what needs to be built, and what effort it will take.
- ◆ Shape and evaluate technology partnerships (e.g. data providers, cloud and AI platforms, systems integrators), including strategic rationale, technical fit, integration approach, and joint solution design.
- ◆ Represent ’s platform and roadmap credibly in executive and technical forums, clearly positioning what the platform does today, what is configurable, and where it is heading.
- ◆ Turn recurring demand into solution playbooks anchored in specific R&D and commercial workflows, including:
- ◇ Offering definition, use cases, and packaging
- ◇ Qualification and fit criteria
- ◇ Scoping templates and success plans
- ◇ Delivery blueprints, reference architectures, and enablement assets
- ◆ Standardise complex offerings (e.g. Private Data, enterprise integrations, and AI workflows on customer data) into productised motions with clear patterns, governance, and pilot-to-rollout pathways.
- ◆ Partner with Product, Engineering, and Data to define feasibility options and reference architectures, and surface clear product and investment recommendations.
- ◆ Co-sponsor a small number of lighthouse opportunities with Sales to validate new solution plays.
- ◆ Orchestrate early cross-functional work (pilot design, governance, decisioning) with the explicit goal of making the play transferable and repeatable, not owning delivery long-term.
- ◆ Drive clean handoff of validated plays into BAU (Sales, CS, Delivery, Product), including training, assets, decision trees, and ownership clarity.
- ◆ Maintain a structured feedback loop into Product on product candidates, investment needs, and learned constraints from enterprise demand.