Senior Product Manager, AI/ML Platform Products

GSK•Cambridge, MA

About The Position

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 AI/ML platform products – the core platform that powers AI/ML model training and deployment across GSK R&D. This role is central to establishing a unified, scalable, and governed enterprise approach to AI/ML, ensuring that R&D teams can efficiently build, evaluate, and operationalize models and ultimately deliver new medicines for our patients.

Requirements

PhD + 2 years, Masters + 4 years, or Bachelors + 6 years
4+ years of experience in product management with a proven track record of delivering AI-powered applications (0-to-1 or scaled products) that solve concrete business or scientific problems in an enterprise or regulated environment.
Experience defining product strategy for modern applications, including experience working closely with data scientists, ML engineers, and domain experts to shape model requirements, model evaluation frameworks, and end-to-end user workflows.
Experience with AI/ML fundamentals, including understanding of model development lifecycles, data pipelines, feature engineering, and MLOps practices—paired with the ability to translate business needs into technical requirements.
Experience integrating AI models into user-facing products, including UX workflows, decision-support tools, automation flows, or scientific applications used by R&D teams.
Experience driving adoption, change management, and measurable business impact for AI solutions across diverse R&D user groups.

Nice To Haves

Direct product management experience building and launching AI/ML-powered applications, including decision-support tools, workflow automation, scientific insight generation, or predictive modeling used by R&D, clinical, or operational teams.
Hands-on experience collaborating with data scientists or ML engineers to define problem statements, model requirements, evaluation approaches, and ML deployment workflows prior to—or alongside—transitioning into product management.
Familiarity with modern ML and transformer-based architectures, with the ability to evaluate trade-offs between off-the-shelf models, open-source models, and domain-specific fine-tuned models depending on performance, regulatory, and data constraints.
Experience developing products that analyze or surface complex, unstructured scientific data, including biomedical text, omics data, imaging, or knowledge graphs.
Working knowledge of bioinformatics, computational biology, or cheminformatics, and a clear vision for how AI-driven applications can accelerate research workflows and scientific decision-making.
Product experience shaping end-to-end ML-driven workflows, including feature pipelines, model serving, monitoring, human-in-the-loop review, and domain-specific UX requirements for scientific users.
Hands-on experience with product management and collaboration tools such as Confluence, Jira, Miro, Monday, or Notion for roadmap, documentation, and cross-functional planning.
Previous experience in life sciences or biopharma R&D is a strong plus.

Responsibilities

Ownership & Strategy Own and drive the product vision, roadmap, and adoption of the AI/ML Platform, delivering core capabilities for model training, fine-tuning, evaluation, deployment, monitoring, and lifecycle management.
Define the strategic direction for foundational AI/ML tooling and ensure platform capabilities meet the needs of diverse R&D model development workflows and scientific applications.
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 to enable shared data standards and consistent data/model lifecycle management.
Coordinate and align product 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.