IT Product Owner, Beijing AI Center

DUTIES & RESPONSIBILITES 1. Data Platform Vision, Strategy & Roadmap • Own the product vision, strategy and multi-year roadmap for the discovery data platform and automation capabilities, aligned with discovery scientific strategy and the global R&D data and IT strategy • Map platform capabilities to the discovery value chain (target ID/validation, hit identification such as HTS, DEL and virtual screening, hit-to-lead, lead optimisation, candidate selection) and to the DMTA cycle, prioritising where data and automation remove the biggest bottlenecks • Define the target architecture and technology stack (e.g. cloud data lakehouse, data ingestion and integration services, metadata and ontology management, knowledge graphs, APIs, analytics and ML/AI tooling) and make build/buy/reuse decisions in line with enterprise architecture • Turn scientific needs into an outcome-based roadmap with clear OKRs and measurable value hypotheses 2. Discovery Data Management & FAIR Data • Own the end-to-end discovery data lifecycle: capture, ingestion, curation, integration, storage, access and archiving of chemistry, biology, assay, screening, imaging, omics and structural data • Drive FAIR data practice: data models, metadata standards, controlled vocabularies and ontologies, and adoption of industry standards (e.g. Allotrope data formats) so data is findable, linked and reusable across projects • Build reusable, well-governed data products (e.g. compound, assay results, target and project data) with clear ownership, quality rules and service levels • Integrate the platform with core discovery systems such as ELN, LIMS, compound registration and sample management, assay data analysis tools and scientific data management systems 3. Lab & Data Automation • Own the automation roadmap for instrument-to-cloud data capture , removing manual data handling and transcription between instruments, analysis tools and the platform • Partner with lab automation teams to connect robotics, liquid handlers, scheduling software and automated chemistry and screening workflows to the data platform, using standards such as SiLA where possible • Automate data pipelines and workflows (ingestion, processing, QC, analysis, reporting) with modern orchestration tools, and support closed-loop / self-driving lab concepts where AI designs the next experiment • Measure automation benefits (e.g. cycle time, throughput, manual effort removed, data quality) and scale proven solutions across sites 4. AI-Ready Data & AI Enablement • Make discovery data AI-ready to support predictive and generative AI, e.g. property and ADMET prediction, generative molecular design, active learning, image-based phenotypic profiling and biological foundation models • Provide the data foundations for ML and GenAI: curated training datasets, feature stores, model lineage and MLOps/LLMOps integration, together with Data Science & AI teams • Enable GenAI and agentic solutions for discovery scientists, such as natural-language access to data, knowledge graph search and assistants for experiment design and analysis • Ensure AI and data use follow AstraZeneca’s responsible and ethical AI principles and data governance 5. Business Partnership, Backlog & Agile Delivery • Act as the single accountable product partner for discovery stakeholders; understand scientific workflows and pain points through product discovery and user research • Own and prioritise the product backlog ; define epics, user stories and acceptance criteria, and lead cross-functional Agile squads (data and platform engineering, automation, UX, vendors/OSPs) from MVP to scale • Manage dependencies with global platform, integration and security teams; drive adoption, training and change management in the scientific community • Represent China discovery needs in global data platform forums 6. Value, Budget, Compliance & Capability Building • Track platform KPIs and value realisation (e.g. data availability and reuse, automation coverage, DMTA cycle time, AI model adoption) and report to IT and business leadership • Manage vendor and OSP relationships, including SOWs, SLAs/KPIs and performance reviews • Ensure data integrity (ALCOA+), information security, IP protection, and compliance with China regulations on data security, personal information and human genetic resources, including cross-border data transfer; apply GLP/GxP controls where data supports regulated studies • Coach and mentor engineers, analysts and Associate Product Owners, and promote product and data management best practice JOB REQUIREMENTS & COMPETENCIES Job requirements • Academic / Professional qualification • Bachelor’s degree in Computer Science, Data Science, Bioinformatics/Cheminformatics, Chemistry, Life Sciences, Engineering or a related field (essential) • Master’s degree or PhD in Cheminformatics, Bioinformatics, Computational Chemistry/Biology, Data Science or a related field (desirable) Technical / skill training • Product ownership certification (e.g. CSPO, PSPO, SAFe POPM) preferred • Cloud and data platform certification (e.g. AWS, Databricks, Snowflake) and/or AI/ML training is a plus Working experience • 8+ years in pharma/biotech R&D IT, scientific informatics, data engineering or lab automation, including 3+ years as product owner, product manager or platform lead for data products Essential • Solid understanding of the drug discovery process (target ID/validation, hit finding, hit-to-lead, lead optimisation, candidate selection) and the DMTA cycle, and of the data each stage generates • Proven experience owning or leading a data platform (e.g. cloud data lake/lakehouse, data integration, scientific data management) in a research environment • Hands-on knowledge of modern data architecture: cloud (preferably AWS), data pipelines and orchestration, APIs, metadata management, data quality and governance • Practical experience applying FAIR data principles, data modelling an