IT Product Owner, Beijing AI Center

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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 and ontologies to scientific data 
Experience with lab or data automation , such as instrument integration, automated data capture or workflow automation 
Hands-on experience enabling AI/ML or GenAI solutions, with a clear view of data requirements, MLOps and model risks 
Proven track record owning product vision, roadmap and backlog with Agile delivery, plus product budgets and vendors 
Excellent communication and influencing skills with scientists and technologists; high autonomy and able to manage multiple priorities 
Desirable
Familiarity with discovery informatics systems (e.g. ELN, LIMS, compound registration and sample management, assay data analysis platforms, SDMS) 
Experience with cheminformatics or bioinformatics data (chemical structures, SAR, HTS/HCS, imaging, omics, structural biology) 
Experience with lab automation and robotics integration, lab scheduling software and standards (SiLA, Allotrope) 
Experience with knowledge graphs, semantic technologies or biological foundation models 
Familiarity with China data regulations (PIPL, Data Security Law, human genetic resources) and cross-border data transfer 
Experience in global, matrixed organisations 
Language ability
Fluent English and Mandarin Chinese, written and spoken; able to present complex topics to senior international audiences 
Computer literacy 
Working knowledge of Python and SQL; familiarity with data platform tools (e.g. Databricks, Snowflake, Airflow, dbt or equivalents) 
Proficient with Agile and product tools (e.g. Jira, Confluence), BI tools (e.g. Power BI, Spotfire) and everyday GenAI tools 
Others
Key competencies:  
Product and platform mindset with outcome orientation 
Scientific curiosity and understanding of discovery research 
Systems and architectural thinking 
Data-driven decision-making and clear prioritisation 
Stakeholder management and influencing without authority 
Ownership, autonomy and results orientation 
Lives AstraZeneca’s values

Date Posted
30-10月-2026
Closing Date
30-12月-2026

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.

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