# Director, Data Science & AI at AstraZeneca

AstraZeneca is hiring a Director, Data Science & AI in Gothenburg, Sweden. Level rates it Builds AI ●●●●; you can [apply on Level](https://jobsbylevel.com/go/5e53fc5c-9ef1-425d-bedb-ac1ebdc68acd).

AI Level 4, AI centrality 90 out of 100. Sweden - Gothenburg.

## Details

- Company: [AstraZeneca](https://jobsbylevel.com/companies/astrazeneca)
- AI level: AI Level 4 (score 90 out of 100)
- Location: Sweden - Gothenburg
- Posted: October 8, 2026
- Apply: https://jobsbylevel.com/go/5e53fc5c-9ef1-425d-bedb-ac1ebdc68acd

## Description

This role can be based at our hubs in Gothenburg, Sweden; Macclesfield, UK; or Barcelona, Spain. We're building a connected, end-to-end Enterprise AI engine - uniting data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across the whole value chain. Success depends on being exceptional connectors: you'll actively leverage existing capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation. If you thrive in high-collaboration environments where your role is to turn complex, cross-functional problems into reusable, enterprise-wide capabilities - and where the measure of success is adoption and scale, not just innovation - you'll have the platform (and sponsorship) to make it real. What you’ll do Are you ready to harness advanced AI to reinvent how medicines are developed, released, and delivered to patients? Could you turn complex, cross-functional challenges into reusable capabilities adopted across development and supply sites worldwide? This is your opportunity to lead enterprise-scale AI that accelerates decisions and elevates productivity across Late Stage Development CMC and Supply Chain. You will spearhead the delivery of Machine Learning, Foundation Models, and Agentic AI that transform how our teams develop, make, test, and supply medicines to patients. Your success will be measured by adoption, impact, and scale—connecting data foundations, AI technology, and process reinvention to create durable advantages for our end-to-end value chain. Accountabilities: People Leadership: Lead, coach, and develop a high-performing team of Data Scientists, Product Managers and Agentic AI specialists. Establish clear expectations, promote technical excellence, support continuous learning, and create an inclusive environment focused on delivery and impact. Technical Delivery: Lead the application of machine learning, multi-agentic LLM, deep learning, and foundation model techniques to solve practical problems across Late Stage Development, CMC, and Supply Chain. Model Development and Adaptation: Lead the development, adaption, fine-tuning, and training of ML, LLM and foundation models using appropriate data, modelling approaches, and performance objectives. Steer you team to select methods that are proportionate to the business need and suitable for deployment in a regulated environment. Model Evaluation and Validation: Design robust evaluation frameworks to assess model performance, reliability, fairness, explainability, and usability. Use appropriate statistical methods and business-focused measures to demonstrate that solutions are fit for purpose and deliver sustained business impact. Practical Solution Delivery: Lead delivery from problem definition and data exploration through prototyping, validation, deployment, and continuous improvement. Ensure solutions are technically robust, secure, maintainable, and capable of delivering measurable outcomes. AI Product Management: Establish, in partnership with Operations IT an AI Product Management and MLOps operating model that enables rapid iteration, robust deployment, and reliable lifecycle management in the cloud. Customer-Focused Problem Solving: Work closely with customers and subject-matter experts to understand their needs, translate business questions into analytical problems, and deliver solutions that improve decision-making, productivity, quality, or cycle times. Cross-Functional Collaboration: Partner effectively with colleagues across CMC, Supply Chain, Operations IT and other relevant functions to integrate models into business processes and technology environments. Process Reinvention: Partner with business functions across late-stage development, clinical and commercial supply to reimagine ways of working and embed AI into daily operations to drive productivity. Standards and Governance: Define best practices,

The description is cut here. Read the full offer: https://jobsbylevel.com/jobs/director-data-science-ai-at-astrazeneca-024d6e

Source: https://jobsbylevel.com/jobs/director-data-science-ai-at-astrazeneca-024d6e

## Cite this page

Level. https://jobsbylevel.com/jobs/director-data-science-ai-at-astrazeneca-024d6e.

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