IT Product Owner, China R&D

DUTIES & RESPONSIBILITES 1. Product Vision, Strategy & Roadmap   • Own the product vision, strategy and multi-year roadmap for a portfolio of scientific digital, data and AI products, aligned with R&D scientific strategy and the China IT and global R&D IT strategy  • Map products and capabilities to the Clinical development lifecycle (Phase III, regulatory submission and lifecycle management) to find where digital and AI can remove bottlenecks and speed up decisions  • Define the target technology stack for the domain (e.g. scientific computing/HPC and cloud, omics and biomarker data pipelines, translational and clinical data platforms, analytics and ML/AI tooling) and make build/buy/reuse decisions aligned with enterprise architecture  • Turn long-term scientific plans into a prioritised, outcome-based roadmap with clear OKRs and measurable value hypotheses  2. Business Partnership & Scientific Domain Leadership   • Act as the single accountable product partner for supported scientific teams, building trusted relationships from working level to senior scientific leadership  • Develop a deep understanding of scientific workflows and data flows (e.g. NGS/multi-omics analysis, biomarker strategy, PK/PD and quantitative modelling, clinical data review and statistical analysis) and use product discovery and user research to uncover high-value opportunities  • Shape demand into business cases, value hypotheses and investment proposals; manage stakeholder expectations and trade-offs transparently  • Represent China R&D needs in global product forums, so that local requirements (e.g. NMPA submissions, China data regulations) are built into global products  3. Product Backlog & Agile Delivery   • Own and prioritise the product backlog ; define epics, user stories and acceptance criteria, and make timely scope and release decisions that maximise value  • Lead cross-functional Agile squads (engineering, data, UX, vendors/OSPs) and drive iterative delivery from MVP to scale  • Coordinate dependencies with platform, data, infrastructure, security and global product teams; manage risks, issues and release readiness  • Drive user adoption, training and change management so products become part of day-to-day scientific work  4. Data & AI Innovation   • Identify, assess and prioritise AI/ML and GenAI use cases (e.g. LLM-based literature and knowledge mining, agentic workflows for scientific data analysis, AI-assisted biomarker discovery, support for protocol and document authoring)  • Take AI use cases from proof of concept to production, covering data readiness, model evaluation, human-in-the-loop design and MLOps/LLMOps  • Champion FAIR data , data quality and reusable data products so scientific data can power analytics and AI across development phases  • Ensure AI solutions follow AstraZeneca’s responsible and ethical AI principles and AI governance processes  5. Value, Budget & Vendor Management   • Own the product portfolio budget: annual planning, Mid-Term Plan (MTP) inputs, forecasting, actual-vs-plan tracking and variance analysis  • Track product KPIs and value realisation (e.g. cycle-time reduction, adoption, cost avoidance, decision quality) and report outcomes to IT and business leadership  • Apply Cloud FinOps discipline to optimise cloud consumption, run cost and licence spend  • Manage vendor and OSP relationships for the portfolio, including SOWs, SLAs/KPIs and performance reviews  6. Compliance, Quality & Capability Building   • Ensure products meet GxP and computer system validation/assurance requirements (e.g. GAMP 5, 21 CFR Part 11, EU Annex 11) where applicable, together with information security and data privacy standards  • Ensure compliance with China regulations on personal information, data se