IT Product Owner, BAIC

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 manageme