The Onyx Research Data Tech organization represents a major investment by GSK R&D and Digital & Tech, designed to deliver a step-change in our ability to leverage data, knowledge, and prediction to find new medicines. We are a full-stack shop consisting of product and portfolio leadership, data engineering, infrastructure and DevOps, data / metadata / knowledge platforms, and AI/ML and analysis platforms, all geared toward:
Building a next-generation data experience for GSK’s scientists, engineers, and decision-makers, increasing productivity and reducing time spent on “data mechanics”
Providing best-in-class AI/ML and data analysis environments to accelerate our predictive capabilities and attract top-tier talent
Aggressively engineering our data at scale to unlock the value of our combined data assets and predictions in real-time
Onyx Product Management is at the heart of our mission, ensuring that everything from our infrastructure, to platforms, to end-user facing data assets and environments is designed to maximize our impact on R&D. The Product Management team partners with R&D stakeholders and Onyx leadership to develop a strategic roadmap for all customer-facing aspects of Onyx, including data assets, ontology, Knowledge Graph / semantic search, data / computing / analysis platforms, and data-powered / LLM-enabled applications.
We are seeking an experienced Senior Product Manager to lead the strategy and delivery of GenAI platform products – the core platform that enables development and deployment of GenAI-powered applications, agents, and MCP services. This platform provides unified access to LLM, embeddings, vector search prompt orchestration, model routing, and agent frameworks, enabling R&D teams to rapidly prototype, operationalize, and scale GenAI solutions and ultimately deliver new medicines for our patients.
Ownership & Strategy
Own and drive the vision, roadmap, development, and adoption of GenAI platform capabilities, ensuring a unified, governed, and high-quality experience for LLMs, embeddings, vector search, prompt orchestration, model routing, agent frameworks, and MCP services.
Define the strategic direction for GenAI capabilities, enabling scalable, compliant, production-ready GenAI and agentic applications across R&D.
Customer & Stakeholder Engagement
Conduct ongoing customer discovery with scientists and AI/ML practitioners to identify emerging needs and translate them into actionable product requirements.
Lead technical product discussions with engineering and scientific leaders to clarify objectives and shape platform direction.
Product Planning & Delivery
Collaborate with stakeholders to define platform features, requirements, and success criteria aligned with scientific use cases and business goals.
Drive agile product execution with engineering and program teams, owning prioritization, backlog management, and delivery of high-quality platform releases.
Platform Integration & Governance
Ensure seamless integration with the Data Platform and AI/ML Platform to enable shared data standards, consistent data and model lifecycle management, and full interoperability across GenAI-powered applications.
Coordinate and align roadmap with R&D platforms to ensure interoperability, governance alignment, and a unified enterprise data, compute, AI, and application ecosystem.
Launch, Adoption & Optimization
Lead platform launches and change-management activities to ensure clear communication, training, and successful adoption across R&D.
Monitor platform usage and performance, analyze feedback and telemetry, and drive continuous improvements to enhance usability, reliability, and scientific impact.
We are looking for professionals with these required skills to achieve our goals
PhD + 2 years, Masters + 4 years, or Bachelors + 6 years
4+ years of experience in product management with a proven track record of shipping 0-to-1 platform capabilities powered by GenAI, LLMs, or autonomous agents in a commercial or large-scale enterprise setting.
Experience defining platform strategy for modern GenAI systems, including hands-on familiarity with core technologies such as RAG pipelines, embedding services, prompt templates, agent frameworks, vector databases, and evaluation tooling.
Knowledge of cloud-native architectures (e.g., AWS, Azure, GCP), API design, high-performance serving infrastructure, and platform components required to securely deploy and scale LLM-based applications for enterprise use.
Experience working closely with platform engineering, MLOps, and security teams to build reliable, governed, reusable GenAI capabilities that accelerate development for multiple downstream product teams.
Experience driving platform adoption, governance, and developer enablement, including SDKs, templates, guardrails, and onboarding materials for cross-functional teams.
If you have the following characteristics, it would be a plus:
Direct product management experience designing and launching GenAI agents and platform capabilities that leverage tool use (APIs, function calling), planning modules, and multi-step reasoning to support a broad set of enterprise or scientific workflows.
Hands-on software engineering or data science experience within a GenAI or ML platform team prior to transitioning into product management, with exposure to LLM infrastructure, RAG pipelines, and developer tooling.
Deep familiarity with modern transformer-based model architectures, with the ability to make platform-level strategic decisions between proprietary models (e.g., GPT-4, Claude), open-source models (e.g., Llama, Mistral), domain-adapted models, and fine-tuning approaches.
Experience delivering platform capabilities that manage, index, or interpret complex, unstructured biomedical or scientific data through embeddings, vector stores, or structured retrieval frameworks.
Extensive knowledge of bioinformatics, computational biology, or cheminformatics, and a strong vision for how enterprise-scale GenAI platforms can power the next generation of scientific automation and agentic workflows.
Extensive platform product experience designing, optimizing, and implementing Model Context Protocols (MCP) or similar orchestration frameworks for LLM-powered agents, including strategies for context management, memory systems, prompt optimization, safety, and maintaining coherence over long-running tasks.
Hands-on experience with product management and technical collaboration tools such as Confluence, Jira, Miro, Monday, Notion, and Git-based documentation.
Previous experience in life sciences or biopharma R&D is a strong plus.
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