【AstraZeneca】【CET】Director, Advanced Analytics & AI, Customer Experience & IT
Astrazeneca
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This role on the market
32 open astrazeneca roles across 1 companies are on ApplySarthi right now.
What astrazeneca roles keep asking for: Stakeholder management (22%), Generative AI (12%) — counted across their open postings here.
CRM jobs · Generative AI jobs · MLOps jobs · Observability jobs
Astrazeneca has 1,539 open roles listed here.
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- R2R Global Process Delivery and Change Manager
- Senior Analyst - Commercial Pharma Analyticschennai
- 【AstraZeneca】【Q&SC】Senior Compliance Specialist,GQP品質保証部
- Payroll Senior Specialist - Australia & New Zealand (ANZ)
Counted across 14 company job boards, updated as roles open and close.
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Interviews for astrazeneca roles keep coming back to Stakeholder management, Generative AI. Practise those questions before you sit with Astrazeneca.
Questions you are likely to be asked
- Why do you want to join Astrazeneca?
- What is your experience with Generative AI? Tell me one thing you learned the hard way.
- How do you handle a problem you do not know the answer to?
- How do you decide which ticket to work on first?
- Tell me about feedback from customers that you passed on to the product team.
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Practise the 【AstraZeneca】【CET】Director, Advanced Analytics & AI, Customer Experience & IT at Astrazeneca interview free →Job purpose As a leadership team member within Customer Experience & IT (CET), the Director – Advanced Analytics & AI is accountable for designing, building, and scaling modern AI capabilities — including agentic AI systems, generative AI applications, predictive analytics, and intelligent workflow automation — across Commercial, Medical, and Enabling functions. This role leads the end-to-end lifecycle of AI-driven capabilities, ensuring they are, Business-relevant, Technically robust, Governed and compliant, and Scalable beyond pilot. The role transforms AI from isolated experiments into enterprise-grade intelligent applications embedded in daily decision-making. Role and Responsibilities 1) AI & Intelligent Application Strategy Define Japan’s AI roadmap covering: Agentic AI systems Generative AI applications Decision intelligence Predictive and prescriptive analytics Identify high-impact use cases aligned with business priorities Ensure AI initiatives are outcome-driven, not technology-driven Align with global AI architecture and governance principles 2) Agentic AI & Modern AI Systems Design and deploy AI agents that: Perform multi-step reasoning Interact with enterprise systems (CRM, Data Lake, content repositories) Automate workflows with human-in-the-loop governance Define orchestration patterns for agent collaboration Establish guardrails for autonomy levels (Human-led / Co-led / AI-led) Ensure auditability, explainability, and logging standards This role typically leads early-stage and capability-defining initiatives, not incremental enhancements. 3) Generative AI & Knowledge Systems Develop RAG-based assistants for: Strategy drafting Insight extraction Field planning Content optimization Define vector architecture, retrieval standards, and prompt governance Ensure safe and compliant usage in regulated environments Drive reusable AI building blocks rather than isolated bots 4) Advanced Analytics & Predictive Modeling Lead development of models including: Segmentation & targeting Potential estimation Forecasting & scenario simulation Impact & KPI gap analysis Establish model lifecycle standards (validation, drift monitoring, retraining) Partner with Data & BI for production data pipelines 5) AI Governance & Risk Management Define evaluation protocols (offline & live) Monitor bias, drift, hallucination, and performance degradation Ensure compliance with regulatory, privacy, and MLR standards Maintain model and agent registry with full traceability 6) From PoC to Scalable AI Capability Collaborate with Strategy & Demand on business framing Run or co-run AI PoCs with clear value metrics Define Scale-ready artifacts (SLOs, monitoring, rollback strategy) Establish LLMOps / MLOps standards for sustainable deployment 7) AI Capability Building Build and lead AI engineers, data scientists, and AI product leads Promote AI literacy across business stakeholders Establish internal reusable AI components (agent templates, evaluation kits) Foster disciplined experimentation culture Requirements Education MS or PhD in Data Science, AI, Computer Science, or related field preferred Experience 12+ years in analytics, AI, or engineering leadership Hands-on experience deploying AI systems beyond experimentation Experience in regulated industry preferred Proven ability to drive business adoption of AI Technical Expertise Agent frameworks and orchestration Generative AI, RAG, vector databases MLOps / LLMOps API-first and event-driven architecture Predictive modeling and statistical learning Observability and monitoring frameworks Business Skills Strong commercial acumen Ability to translate AI into measurable impact Executive communication Portfolio prioritization Language Fluent Japanese; business-level English Career Level F Location Osaka or Tokyo Date Posted 06-3月-2026 Closing Date 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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Listed on workday · posted 2026-10-12. ApplySarthi collects openings and links to application pages; the role is advertised by Astrazeneca, not by us.