Senior Product Manager, AI​/ML Platform Products

Senior Product Manager, AI/ML Platform Products

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.

Key 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.
• 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.

Basic Qualifications:

• 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.
• Knowledge of AI/ML fundamentals, including understanding of model development life cycles, 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.

Preferred Qualifications:

• 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…