Senior Scientist (m/f/d), Computational Pathology Biomarker Lead (Oncology) Full-time

Senior Scientist (M/F/D), Computational Pathology Biomarker Lead (Oncology)

AstraZeneca is a global, science-led, patient-focused biopharmaceutical company that focuses on the discovery, development and commercialisation of prescription medicines for some of the world's most serious diseases. At Computational Pathology Munich (CPM), we make a significant contribution to high-performance, data-driven research and development. Our team operates in a demanding, fast-paced environment where excellent collaboration, clear communication and precise organization are critical.

AstraZeneca's Enterprise AI organization is shaping the future of drug development with an integrated AI engine that connects data, technology, and expertise to accelerate breakthroughs. In Computational Pathology and Biomarkers, we deliver AI-driven solutions for patient selection, biomarker development, and clinical decisions on a scale. We collaborate across the organization to reuse capabilities and scale innovation globally measuring success by adoption, impact, and measurable outcomes across therapeutic areas and regions.

We are looking for a Senior Scientist (M/F/D), Computational Pathology Biomarker Lead (Oncology) to drive the development of AI-enabled computational pathology and multimodal biomarkers that transform patient selection and drug development across our Oncology portfolio. This role is based at our Munich, Germany office.

In this role, you will collaborate with multidisciplinary teams to pioneer data-driven approaches that generate impactful insights and improve clinical outcomes. You will contribute to an industry-leading portfolio of targeted therapy programs, from early development through to lifecycle management of marketed therapies.

Key Responsibilities:

• Lead and collaborate with cross-functional teams to generate digital biomarker signatures from complex datasets using AI/machine learning, enabling target engagement assessment, patient stratification, and early signals of biological activity.
• Drive biomarker discovery, development, and implementation across Oncology programs, using advanced computational pathology technologies to guide indication selection and identify target patient populations.
• Apply deep understanding of cancer biology to interpret clinical samples, support rational drug combinations, and identify mechanisms of resistance.
• Use strong statistical and analytical expertise to develop novel image-derived metrics and uncover meaningful patterns in complex, multimodal datasets.
• Integrate tissue, imaging, genomics, and clinical data to inform project strategy and clinical decision-making.
• Support analytical validation frameworks for multimodal biomarkers, enabling regulatory submissions and clinical implementation.
• Contribute to project quality through data review, reporting, presentations, publications, and input into clinical and regulatory documents.
• Support vendor selection, companion diagnostics development, and biomarker strategies for clinical studies.
• Drive innovation by evaluating and implementing new AI technologies and improving computational pathology workflows.
• Share expertise by training peers, promoting reproducible research, and contributing to the external scientific community.
• Lead scientific discussions and present progress and insights to project teams and leadership.

Experience and Capabilities:

• PhD in Biological Sciences, Computational Biology, Bioinformatics, or a related field, with demonstrated experience at the interface of biology and data science.
• Strong expertise in computational pathology and AI-driven image analysis, particularly histopathology, with hands-on implementation experience.
• Deep understanding of cancer biology, including tumor microenvironment, immune pathways, and mechanisms of resistance.
• Proven ability to generate insights from complex, multi-modal datasets, including tissue, imaging, genomic, and clinical data.
• Proficiency in programming (Python/R) and advanced data analysis, including clinical biostatistics (e.g., survival analysis, feature selection, cross-validation, visualization).
• Experience with data management best practices, including curation, data engineering, FAIR principles, and reproducible research.
• Solid understanding of clinical trials and oncology drug development, with experience contributing to clinical research strategies.
• Strong collaboration and influencing skills, with the ability to work effectively in cross-functional, fast-paced environments.
• Excellent scientific communication skills, with the ability to present complex data clearly to both specialist and non-specialist audiences.
• Outstanding analytical, organizational, and problem-solving capabilities.
• Ability to prioritize effectively, remain highly productive, and balance innovation with timely project delivery in a dynamic environment.
• Demonstrated success contributing to interdisciplinary, cross-functional projects.

Desirable:

• Experience in the pharmaceutical or biotechnology industry, particularly in translational medicine or biomarker development.
• Hands-on experience applying machine learning/AI approaches to image analysis and biomarker discovery.
• Understanding of clinical trial design, biomarker validation strategies, and regulatory requirements for AI-enabled biomarkers.
• Proven publication record in peer-reviewed journals demonstrating expertise in computational pathology and/or AI.
• Formal training in computational biology, bioinformatics, data science, AI, or machine learning.
• Familiarity with approaches for integrating multimodal biomarkers.

What you can expect:

• Individual development opportunities with a focus on lifelong learning
• Trust, appreciation, and room to shape things in a focused and passionate team
• Modern office space in Munich enabling collaborative, flexible, and agile work
• A diverse, inclusive, and bias-free work environment, actively welcoming applications from all qualified candidates, regardless of background or characteristics