Location Munich, Bavaria, Germany
Job ID R-228651
Date posted 09/06/2025
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. But we’re more than one of the world’s leading pharmaceutical companies.

SITE DESCRIPTION - MUNICH, GERMANY

Welcome to Computational Pathology Munich, one of over 400 sites here at AstraZeneca, providing a collaborative environment where everyone feels comfortable and able to be themselves is at the core of AstraZeneca’s priorities, it’s important to us that you bring your full self to work every day. To help you maintain your best self, here’s a sneak peek into some of the things this site provides for you: After-work events, lunch & learns, spacious environment, sustainable office working environment, events, family and childcare support and of course the Alps around the corner for hiking, biking and skiing.

GENERAL ACCOUNTABILITIES

As a DMO Specialist you will support new drug and companion diagnostics development initiatives in oncology. Your primary responsibility is to manage, curate, integrate, and analyze data from various sources, maintaining quality and compliance within computational pathology workflows. You will collaborate with both internal and external stakeholders to enhance data resources and facilitate high-quality analysis and informed decision-making.

• Data Integration and Management: Manage and integrate diverse datasets, including imaging data, patient/sample metadata, real-world evidence, clinical trials, and image analysis results ensuring data consistency, quality and validation and preparing data in view of AI model training operations.
• Data/Image analysis: Perform tasks related to biomarker identification and quantification by analyzing whole slide images (e.g., immunohistology), using state of the art AI deep learning models as part of a multidisciplinary team (composed of biologists, physicians, AI scientists, etc.)
• Technology and Tools: Stay updated on the latest data management technologies and tools, and evaluate and recommend new data management solutions to improve efficiency and effectiveness.
• Collaboration and Communication: Establish strong working relationships with teams such as machine learning specialists, data scientists, engineers, software developers, and clinical/biological researchers. Ensure effective utilization of data resources and communicate results clearly, addressing uncertainties and limitations.
• Compliance and Regulatory Requirements: Ensure that processes comply with relevant data governance and quality standards, especially within a computational pathology environment. Document all steps of the work to ensure compliance to highest quality assurance standards. Support further development and automation of our quality management system.