Product Manager II - Molecule Design Products
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
- Contribute to Product Development & Adoption: Actively contribute to the full product lifecycle, from development to launch and adoption, focusing on specific features and components within novel molecule design solutions for the scientific community at GSK.
- Support GenAI Strategy: Support the strategic integration and enhancement of GenAI capabilities within molecule design tools, helping to define and implement next-generation AI-powered functionalities.
- Collaborative Delivery: Partner closely with Onyx tech teams, R&D scientists, and leaders to facilitate the delivery of impactful cloud-based products and solutions that leverage Generative AI and agentic capabilities.
- Product Strategy & Roadmap Contribution: Contribute to the definition and execution of specific features and components within the molecule design solutions roadmap, ensuring alignment with the overall product strategy.
- User Research & Feedback Analysis: Conduct user interviews, gather feedback, and analyze user data to inform the definition of product enhancements and identify opportunities for iterative improvements in molecule design tools.
- Product Feature Definition: Work closely with Senior Product Managers and engineering teams to translate user needs into clear, well-defined product requirements, user stories, and acceptance criteria for discrete features.
- Agile Development Engagement: Actively participate in agile ceremonies (e.g., sprint planning, backlog refinement, stand-ups) with engineering teams, ensuring product requirements are understood and supporting effective backlog management.
- GenAI Feature Implementation Support:
- Contribute to the development and implementation of specific features within AI Agents, leveraging LLMs and Generative AI to automate well-defined parts of scientific research tasks.
- Assist in the design and testing of human-agent interaction components, focusing on specific conversational flows or user interface elements to enhance usability.
- Support the product lifecycle for individual models or agents by assisting with data gathering, testing of fine-tuned models, and developing documentation for APIs/agents.
- Support the implementation of Model-In-The-Loop designs by gathering R&D user feedback and contributing to the
- Participate and contribute in highly technical product discussions with engineering leaders, translating ambiguous scientific objectives into precise requirements for fine-tuning foundational models, vector databases, and multi-agent system architectures.
- Cross-Functional Coordination: Coordinate with both tech and RD teams, including DevOps& Infrastructure, data engineering, computing platform engineering, data & knowledge platform engineering, program management teams and RD data leadership teams, to align product strategies, gather input, ensuring clear communication and smooth execution.
- Product Release Support: Assist with product launch activities for new features, including preparing documentation, training materials, and support resources to ensure successful adoption.
- Performance Monitoring & Optimization: Monitor key metrics for specific product features, gather user feedback on performance, and identify potential areas for improvement.
- Bachelors degree in Bioinformatics, Computational Biology, cheminformatics, AI/ML, Computer Science, Software Engineering, or related discipline.
- Experience in product management with a proven track record of shipping 0-to-1 software products powered by AI/GenAI, LLMs, or autonomous agents in a commercial or large-scale enterprise setting.
- Demonstrated experience executing product strategy for modern applications, including hands-on experience with technologies core to AI systems such as vector databases, MLOps, retrieval-augmented generation, and model fine-tuning.
- Demonstrated technical fluency with cloud-native architectures (e.g., AWS, GCP, Azure), API design, and the infrastructure required to serve and scale LLM-based applications.
- Master’s degree or PhD in Bioinformatics, Computational Biology, Computational Chemistry, Data Science, Computer Science/Software Engineering, or related discipline Engineering, Cloud Computing or related discipline.
- Experience contributing to products that involve AI agents, their tool utilization (APIs, function calling), or the development of conversational AI interfaces.
- Hands-on software engineering or data science experience in an AI/GenAI-focused team prior to transitioning into product management.
- Familiarity with the architecture of modern transformer-based models and an understanding of the strategic trade-offs when selecting between proprietary, open-source, or fine-tuned custom models.
- Experience contributing to products that manage or interpret complex, unstructured biomedical data.
- Familiarity with Model Context Protocols (MCP) for LLM-powered agents, including basic concepts of prompt engineering, context window management, and maintaining model coherence in multi-turn interactions.
- Foundational knowledge of bioinformatics, computational biology, or cheminformatics, and an interest in how agentic AI can impact drug discovery.
- Hands-on experience with product management tools such as Confluence, Jira, Miro, Monday, Notion, etc.
- Previous experience in the life science industry or biopharma R&D is a plus.