# IT Product Owner, BAIC at AstraZeneca

AstraZeneca is hiring an IT Product Owner, BAIC. Level rates it Works on AI ●●●○; you can [apply on AstraZeneca](https://jobsbylevel.com/go/e40e4693-fd24-43c4-87eb-aa253e1115bb?utm_medium=ai-agent).

AI Level 3, AI centrality 65 out of 100. Shanghai Jing'An Office.

## Details

- Company: [AstraZeneca](https://jobsbylevel.com/companies/astrazeneca)
- AI level: AI Level 3 (score 65 out of 100)
- Location: Shanghai Jing'An Office
- Posted: October 29, 2026
- Apply: https://jobsbylevel.com/go/e40e4693-fd24-43c4-87eb-aa253e1115bb?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=it-product-owner-baic-at-astrazeneca-2a4b0a

## Prepare for this job

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## Description

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

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