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AstraZeneca

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

AstraZeneca is hiring a 【AstraZeneca】【CET】Director, Advanced Analytics & AI, Customer Experience & IT in Osaka, Japan. Level rates it ; you can apply on AstraZeneca.

Level's read on this job

  • Level rates this role Builds AI ●●●●: the job is building AI systems.
  • Tools and skills the posting cites include RAG, AI agents and MLOps.
  • Level classifies it as an executive role.
  • Level lists 1,300+ other open roles at AstraZeneca.

AI in this role

ragai-agentsml-ops

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.

How we rate this

【AstraZeneca】【CET】Director, Advanced Analytics & AI, Customer Experience & IT at AstraZeneca rates 93 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGAI agentsML Ops

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. How do you monitor a model once it's live, and how do you know it needs retraining?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • List these exact terms on your resume: RAG, AI agents, and ML Ops. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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