# Senior Data Scientist – Build the AI That Powers Life-Saving Medicine Production at AstraZeneca

AstraZeneca is hiring a Senior Data Scientist – Build the AI That Powers Life-Saving Medicine Production. Level rates it Builds AI ●●●●; you can [apply on AstraZeneca](https://jobsbylevel.com/go/b7e828fa-4698-4749-b365-a15839c4c15c?utm_medium=ai-agent).

AI Level 4, AI centrality 93 out of 100. Sweden - Gärtuna.

## Details

- Company: [AstraZeneca](https://jobsbylevel.com/companies/astrazeneca)
- AI level: AI Level 4 (score 93 out of 100)
- Location: Sweden - Gärtuna
- Posted: November 10, 2026
- Apply: https://jobsbylevel.com/go/b7e828fa-4698-4749-b365-a15839c4c15c?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-data-scientist-build-the-ai-that-powers-life-saving-medicine-production-at-astrazeneca-5ddb93

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## Description

Data, Analytics & AI, Sweden Operations - Södertälje, Sweden Do you want your models, agents and AI systems to run in the factory that makes medicines for millions of patients? At AstraZeneca, we follow the science and put patients first. We are building the next generation of AI in pharmaceutical manufacturing and supply, and we are looking for a Senior Data Scientist who can help us take it from promising prototype to trusted production reality. About the role Södertälje is one of the largest sites in AstraZeneca’s global network and a recognised World Economic Forum Global Lighthouse for digital manufacturing. Sweden Operations is now moving beyond individual predictive models towards connected, AI-enabled operations: decision support that senses what is happening across the value chain, reasons about it and recommends or takes action, with people in control. As Senior Data Scientist in the Data, Analytics & AI team, part of Strategy & Performance, you will be a technical leader in that shift. You will work with colleagues across Inbound, Manufacturing, Quality, Outbound, Maintenance, Supply and Production Planning to turn complex operational problems into AI solutions that are reliable, explainable and compliant, and that people actually use. What you’ll do Design and build AI solutions end to end. Combine machine learning, optimisation, simulation and generative AI to solve real operational problems, from forecasting and anomaly detection to document intelligence and process optimisation. Develop LLM and agentic AI applications. Build retrieval-augmented and agent-based systems that work with our data, tools and procedures, with rigorous evaluation, guardrails and human-in-the-loop design. Frame the problem before the model. Translate unstructured business challenges into the right analytical approach, including when the answer is a simple model rather than a complex one. Take solutions into production. Own the path from prototype to a monitored, maintained service using modern MLOps/LLMOps practices on our cloud and data platforms. Make AI trustworthy. Build quality, explainability, validation and documentation into the work so that it meets GxP, data integrity and regulatory compliance. Influence. Communicate results, uncertainty and limitations clearly to stakeholders from the shop floor to senior leadership and help them decide where AI will and will not add value. Keep the team ahead. Follow research and tooling developments, test what is useful, and bring it into our ways of working. Who you are You are curious, rigorous and pragmatic. You like hard, ambiguous problems, but you care more about measurable impact than about novelty. Essential Master’s degree (or equivalent experience) in mathematics, statistics, computer science, engineering, physics, operations research or a related quantitative field. Several years of experience delivering data science solutions into production, with a track record of business or scientific impact. Strong Python and SQL skills, with solid software engineering practices, including Git-based version control, testing, code review, CI/CD, Docker, and Kubernetes. Strong data engineering skills, including building and maintaining reliable data pipelines, working with complex data sources, and ensuring data quality for analytics and AI applications. Deep knowledge of machine learning and statistics, including supervised and unsupervised learning, with experience in some of time series, forecasting or deep learning, and sound judgement on model choice, validation and uncertainty. Hands-on experience building LLM-powered applications, including RAG, tool calling, and agentic workflows and orchestration, with a disciplined approach to evaluation, regression testing, and monitoring. Experience deploying and operating models and AI services in a cloud environment, including CI/CD, containerisation, monitoring, and pipeline orchestration. Excellent communication and stakeholder skills, and the

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