{"id":3467839,"url":"https://alion.io/job/astrazeneca-it-product-owner-baic-2","title":"IT Product Owner, BAIC","company":{"id":13195,"name":"AstraZeneca","domain":"astrazeneca.com","url":"https://alion.io/company/astra-zeneca","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":92,"open_postings":150,"ghost_share":0.007,"stale_share":0.433,"repost_share":0.093,"time_to_fill_p50_days":19,"computed_at":"2026-10-30T05:48:23Z"}},"role":"Product","role_family":"Product","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Shanghai, China"],"countries":["CN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":33000,"max_usd":86000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1365},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Feature Store","optional":false},{"name":"GLP","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LIMS","optional":false},{"name":"LLMOps","optional":false},{"name":"Platform Engineering","optional":false},{"name":"AWS","optional":true},{"name":"Confluence","optional":true},{"name":"Databricks","optional":true},{"name":"dbt","optional":true},{"name":"Jira","optional":true},{"name":"Power BI","optional":true},{"name":"Python","optional":true},{"name":"Snowflake","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-10-29T15:56:07Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-10-30T05:01:30Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"DUTIES & RESPONSIBILITES\n1. Data Platform Vision, Strategy & Roadmap\nOwn 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 \nMap 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 \nDefine 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 \nTurn scientific needs into an outcome-based roadmap with clear OKRs and measurable value hypotheses \n2. Discovery Data Management & FAIR Data\nOwn 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 \nDrive 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 \nBuild reusable, well-governed data products (e.g. compound, assay results, target and project data) with clear ownership, quality rules and service levels \nIntegrate the platform with core discovery systems such as ELN, LIMS, compound registration and sample management, assay data analysis tools and scientific data management systems \n3. Lab & Data Automation\nOwn the automation roadmap for instrument-to-cloud data capture, removing manual data handling and transcription between instruments, analysis tools and the platform \nPartner 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 \nAutomate 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 \nMeasure automation benefits (e.g. cycle time, throughput, manual effort removed, data quality) and scale proven solutions across sites \n4. AI-Ready Data & AI Enablement\nMake 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 \nProvide the data foundations for ML and GenAI: curated training datasets, feature stores, model lineage and MLOps/LLMOps integration, together with Data Science & AI teams \nEnable GenAI and agentic solutions for discovery scientists, such as natural-language access to data, knowledge graph search and assistants for experiment design and analysis \nEnsure AI and data use follow AstraZeneca’s responsible and ethical AI principles and data governance \n5. Business Partnership, Backlog & Agile Delivery\nAct as the single accountable product partner for discovery stakeholders; understand scientific workflows and pain points through product discovery and user research \nOwn 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 \nManage dependencies with global platform, integration and security teams; drive adoption, training and change management in the scientific community \nRepresent China discovery needs in global data platform forums \n6. Value, Budget, Compliance & Capability Building\nTrack 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 \nManage vendor and OSP relationships, including SOWs, SLAs/KPIs and performance reviews \nEnsure 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 \nCoach and mentor engineers, analysts and Associate Product Owners, and promote product and data management best practice \nJOB REQUIREMENTS & COMPETENCIES\nJob requirements\nAcademic / Professional qualification \nBachelor’s degree in Computer Science, Data Science, Bioinformatics/Cheminformatics, Chemistry, Life Sciences, Engineering or a related field (essential) \nMaster’s degree or PhD in Cheminformatics, Bioinformatics, Computational Chemistry/Biology, Data Science or a related field (desirable) \nTechnical / skill training\nProduct ownership certification (e.g. CSPO, PSPO, SAFe POPM) preferred \nCloud and data platform certification (e.g. AWS, Databricks, Snowflake) and/or AI/ML training is a plus \nWorking experience\n8+ 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 \nEssential\nSolid 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 \nProven experience owning or leading a data platform (e.g. cloud data lake/lakehouse, data integration, scientific data management) in a research environment \nHands-on knowledge of modern data architecture: cloud (preferably AWS), data pipelines and orchestration, APIs, metadata management, data quality and governance \nPractical experience applying FAIR data principles, data modelling and ontologies to scientific data \nExperience with lab or data automation, such as instrument integration, automated data capture or workflow automation \nHands-on experience enabling AI/ML or GenAI solutions, with a clear view of data requirements, MLOps and model risks \nProven track record owning product vision, roadmap and backlog with Agile delivery, plus product budgets and vendors \nExcellent communication and influencing skills with scientists and technologists; high autonomy and able to manage multiple priorities \nDesirable\nFamiliarity with discovery informatics systems (e.g. ELN, LIMS, compound registration and sample management, assay data analysis platforms, SDMS) \nExperience with cheminformatics or bioinformatics data (chemical structures, SAR, HTS/HCS, imaging, omics, structural biology) \nExperience with lab automation and robotics integration, lab scheduling software and standards (SiLA, Allotrope) \nExperience with knowledge graphs, semantic technologies or biological foundation models \nFamiliarity with China data regulations (PIPL, Data Security Law, human genetic resources) and cross-border data transfer \nExperience in global, matrixed organisations \nLanguage ability\nFluent English and Mandarin Chinese, written and spoken; able to present complex topics to senior international audiences \nComputer literacy\nWorking knowledge of Python and SQL; familiarity with data platform tools (e.g. Databricks, Snowflake, Airflow, dbt or equivalents) \nProficient with Agile and product tools (e.g. Jira, Confluence), BI tools (e.g. Power BI, Spotfire) and everyday GenAI tools \nOthers\nKey competencies:\nProduct and platform mindset with outcome orientation \nScientific curiosity and understanding of discovery research \nSystems and architectural thinking \nData-driven decision-making and clear prioritisation \nStakeholder management and influencing without authority \nOwnership, autonomy and results orientation \nLives AstraZeneca’s values \nDate Posted\n29-10月-2026Closing Date\n30-12月-2026AstraZeneca 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. 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