【AstraZeneca】【CET】Director, Advanced Analytics & AI, Customer Experience & IT

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 systemsGenerative AI applicationsDecision intelligencePredictive 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 reasoningInteract 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 draftingInsight extractionField planningContent 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 & targetingPotential estimationForecasting & scenario simulationImpact & 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