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.