Associate Director - Product Owner, Engineering, EHS & Sustainability

Overview

Associate Director, Product Owner for Engineering, EHS and Sustainability within GSK's Enterprise Systems team, owning and prioritising the product backlog and leading delivery of product and AI capabilities.

What you'll do

• Partner with business stakeholders to understand functional needs, refine requirements and define product outcomes that support business priorities, within guardrails set by the Product Manager and Product Domain Lead.
• Co-create the product vision and roadmap with the business and own the value case, including expected benefits and how they are measured.
• Own and manage the Product backlog, ensuring requirements (epics, features, user stories) are well-defined and supported by minimum data inputs (cost drivers, user volumes, risk/sensitivity, process impact, dependencies).
• Make prioritisation decisions for the Product backlog based on value, cost, risk, business continuity and resource capacity, escalating only where strategic trade-offs are required by the Product Manager or Product Domain Lead.
• Lead the Product team's delivery activities, guiding Product Analysts and working with Engineers, Architects and Testers to ensure solutions are fit-for-purpose, secure, compliant, supportable and aligned to architectural standards.
• Drive operational readiness by applying a delivery readiness checklist covering support model requirements, training needs, continuity planning, configuration/runbook documentation, release notes and user onboarding.
• Define and maintain the Product performance dashboard, tracking adoption, cycle time/throughput, stability/defects, UX signals (e.g., CSAT, SUS, completion rates) and support effort, and use insights to drive continuous improvement.
• Ensure product changes follow required security controls, risk assessments, data handling policies and regulated change processes, obtaining approvals from relevant SMEs where required.
• Shape design trade-offs with Solution Architects and scout emerging capability on the product platforms, bringing a short ranked opportunity list into backlog refinement.
• Act as an escalation point for the Product team and business users on functional product issues, guiding decisions within agreed financial, risk and architectural guardrails.
• Contribute to improving delivery practices, promoting agile, iterative, value-driven and data-led ways of working, including meaningful measurement of benefits and operational impact.
• Own and prioritise the AI product backlog, translating Engineering, EHS and Sustainability needs into clear AI use cases, epics, features and acceptance criteria.
• Define the business value and success measures for AI use cases, including adoption, user experience, process improvement, quality, risk reduction and support effort.
• Lead AI-enabled product delivery across business stakeholders, engineers, architects, data specialists, security, quality and third-party providers.
• Ensure responsible and compliant use of AI, including human oversight, data protection, security, validation, traceability and regulated change controls.
• Monitor AI product performance after release, using operational metrics, user feedback and evaluation results to identify improvement priorities.

What you'll need

• Bachelor's or Master's degree in a relevant field (e.g., Computer Science, Information Technology or Engineering), or equivalent experience.
• Typically 5+ years of experience in product delivery, digital solutions, business systems or process-led technology roles.
• Proven experience defining and prioritising functional requirements (epics, features, user stories) and managing a product backlog.
• Experience working directly with business stakeholders to translate needs into feasible, value-led technology changes.
• Demonstrated ability to make prioritisation decisions based on business value, process impact, risk, cost and technology constraints.
• Experience leading delivery teams or analysts through the product lifecycle, from refinement and design through testing, release and adoption.
• Familiarity with enterprise governance requirements such as change control, security, data protection and regulatory/compliance considerations.
• Exposure to continuous improvement approaches (e.g., automation, process simplification, user-centred enhancements) that uplift product adoption, stability or user experience.
• Experience working within agile delivery environments and using iterative planning, delivery and release practices.
• Experience owning or delivering an AI, Generative AI, advanced analytics or intelligent automation product.
• Experience translating business problems into prioritised AI use cases, product requirements and measurable outcomes.
• Experience leading cross-functional delivery involving business SMEs, architects, engineers, data teams, security and compliance functions.
• Experience defining and monitoring AI product KPIs, evaluation criteria, adoption measures and benefits realisation.
• Experience delivering technology products in a regulated or highly controlled environment, with appropriate governance, validation and change control.

Nice to have

• Industry certifications highly desirable (e.g., Agile Methodologies, CPM).
• Strong communication and influencing skills, able to explain technology constraints clearly and support stakeholders in making informed product decisions.
• Skilled in coaching and guiding Product Analysts and delivery team members to ensure requirements, documentation and solutions meet business needs and compliance expectations.
• Able to interpret business processes, user feedback and operational data to identify opportunities for process simplification, automation and improved user experience.
• Comfortable working within structured governance, security and data protection requirements, ensuring product changes remain controlled, supportable and compliant.
• Strong analytical mindset, able to weigh value, risk, complexity and cost to make prioritisation decisions and sequence backlog items appropriately.
• AI product management: ability to identify, assess and prioritise AI use cases based on business value, feasibility, data availability, risk and cost.
• Generative AI understanding: good working knowledge of large language models, Generative AI, retrieval-augmented generation, AI agents and human-in-the-loop solutions.
• AI evaluation: ability to define acceptance criteria and performance measures covering accuracy, relevance, consistency and user value.
• Responsible AI and risk management: understanding of privacy, security, transparency, human oversight and risks such as unsupported or misleading outputs.
• Stakeholder communication: ability to explain AI capabilities, limitations and trade-offs clearly to business and technology stakeholders.

Details

• Location: Bengaluru Luxor North Tower, India.