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Salary
≈ $80k – $171k per year (Estimated)
Location
In office (Osaka, Tokyo)
Seniority
Architect · 12+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 27, 2026. First seen by Alion on Mar 6, 2026. AstraZeneca scores A on the Alion truth index.

Overview
Company
Impact
Profile match
AstraZeneca is a British-Swedish pharmaceutical company formed in 1999 by the merger of Astra of Sweden and Zeneca of the United Kingdom, and now one of the largest drug makers in the world by revenue. Its portfolio is concentrated in oncology with Tagrisso, Imfinzi and Enhertu, alongside cardiovascular, renal and metabolic products including Farxiga, respiratory and immunology medicines, and a rare disease business built on the acquisition of Alexion. Headquartered in Cambridge in England and listed in London, Stockholm and New York, it reports in United States dollars.

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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