Associate Principal Data Scientist Full-time

Associate Principal Data Scientist

At AstraZeneca, we put patients first and strive to meet their unmet needs worldwide. If you are swift to action, willing to collaborate, and curious about what science can do, don't hesitate to apply!

In the Pharmaceutical Technology and Development (PT&D) department, you will be a key player in transforming molecules into groundbreaking medical treatments. PT&D leads the charge in developing cutting-edge synthetic routes, drug formulations and delivery technologies, ensuring our products are effective, safe, and of the highest quality.

As an Associate Principal Data Scientist, you'll apply your expertise to lead and support innovative projects that apply machine learning, deep learning, and foundation models to high-value scientific and business challenges. Working in a multidisciplinary environment, you will be instrumental in identifying and developing impactful AI use cases, translating emerging technologies into practical solutions that create measurable value.

The role:

In this role, you will lead projects involving large language models, retrieval-augmented generation, multimodal AI, and scientific knowledge discovery using advanced machine learning and deep learning techniques. Your contributions will be vital in shaping our approach to foundation model adoption and advancing our ability to deliver scalable, responsible, and impactful AI solutions.

The position will be based at Gothenburg, Sweden.

Accountabilities

• Develop methodologies and solutions for AI use cases using machine learning, deep learning, and foundation model techniques.
• Design, build, and evaluate workflows involving large language models, embeddings, vector search, retrieval-augmented generation, prompt engineering, and fine-tuning.
• Apply deep learning approaches to complex structured and unstructured data, selecting appropriate methods based on the problem and business need.
• Create visualisations and other communication materials to support intuitive interpretation of data, model outputs, and results, and to facilitate stakeholder engagement.
• Collaborate with cross-functional teams, ensuring effective knowledge transfer to data engineering, and MLOps teams for solution build, deployment, and lifecycle management.
• Develop robust evaluation approaches for foundation model applications, including assessment of performance, groundedness, factuality, safety, and business impact.
• Keep pace with industry advancements by reviewing academic papers, evaluating emerging technologies, and contributing to internal standard processes and knowledge sharing.
• Communicate technical concepts, limitations, and results to both technical and non-technical audiences.

Essential Requirements

• Advanced degree or equivalent experience in computer science, data science, artificial intelligence, machine learning, deep learning, or related fields.
• Excellent coding skills in languages such as Python.
• Significant industrial experience in data science with a focus on machine learning and deep learning, and experience with ML frameworks such as PyTorch, TensorFlow, or equivalent.
• Strong experience of version control and software engineering best practices, including the use of tools such as Git to support collaborative development, code quality, and maintainability.
• Experience developing data science and AI models and partnering with MLOps or engineering teams to productionise solutions.
• Experience working with structured, unstructured, and knowledge-heavy data, including text-rich sources such as documents, reports, and scientific literature.
• Strong understanding of foundation model opportunities and limitations, including hallucination, bias, privacy, security, and governance considerations.

Desirable Requirements

• Contributions to open-source projects. If you meet this criteria, please highlight merged GitHub PRs in your application.
• Strong publication record in the field of AI, machine learning, deep learning, or generative AI.
• Experience delivering machine learning or foundation model projects with applications in pharmaceutical development, healthcare, life sciences, chemistry, or other scientific domains.
• Experience with one or more applied AI domains such as retrieval-augmented generation, multimodal learning, transfer learning, federated learning, few/zero-shot learning, meta learning, explainable AI.
• Experience evaluating and operationalising open-source and proprietary foundation models.
• Knowledge of responsible AI and model governance approaches in regulated environments.

Here, technology and science meet to deliver impact you can see—faster discovery, smarter development, and better access for patients. We value kindness alongside ambition, encouraging transparent collaboration, continuous learning, and the courage to challenge norms, so your contribution scales beyond a single product and helps redefine what digital, data, and AI can do for healthcare.

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. We balance the expectation of being in the office while respecting individual flexibility.

We welcome your application (CV and cover letter) no later than 20th May 2026. Apply now!