Senior Specialist in Computational Pathology Delivery Operations (m/f/d)

ABOUT ASTRAZENECA

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

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.

We are seeking a Senior Specialist in Computational Pathology Delivery Operations (m/f/d) in Munich to support the execution of computational pathology workflows across AI/ML model development and biomarker discovery programs.

This role is responsible for ensuring the reliable execution of computational pathology workflows, including data ingestion, curation, quality control, and analysis activities. The position plays a key role in ensuring that imaging data, metadata, annotations, and analysis outputs are fit for purpose for AI development, scientific decision-making, and potential regulatory applications.

This role sits at the intersection of pathology, data science, and AI engineering, and collaborates closely with internal teams and external partners to manage complex datasets and maintain high standards of data quality, compliance, and workflow execution.

The ideal candidate is a highly organized and dependable data professional with experience managing scientific or biomedical datasets, strong attention to detail, and the ability to coordinate effectively across diverse stakeholders in a dynamic research environment.

Key Responsibilities

Data Transfer and Curation

• Coordinate with wet laboratories, contract research organizations (CROs), and external collaborators to facilitate the transfer of imaging data and associated metadata.
• Ensure incoming datasets are delivered according to defined data formats, metadata standards, and project requirements.
• Manage and integrate multi-source datasets, including digital pathology images, sample metadata, clinical trial information, real-world data, and image analysis outputs.
• Translate scientific and model development requirements into structured dataset preparation tasks.
• Verify the completeness, accuracy, and quality of datasets in accordance with internal Standard Operating Procedures.
• Ensure imaging data, annotations, and metadata are suitable for downstream AI/ML model development, validation, and scientific analysis.
• Maintain consistency, traceability, and usability of datasets across computational pathology workflows.

Workflow Execution for Computational Pathology

• Execute computational pathology workflows across AI model development and biomarker discovery projects.
• Perform operational data preparation tasks including data pooling, subsetting and splitting, and annotation consolidation.
• Run image analysis pipelines to generate quantitative readouts supporting biomarker discovery and program decision-making.
• Upload and manage analysis results within cloud-based data platforms (e.g., QuartzBio).

Data Governance, Compliance, and Documentation

• Ensure datasets comply with internal policies such as the Global Standard for Human Biological Samples and other relevant governance frameworks.
• Initiate and coordinate data approval processes (e.g., iDAP approvals) with Data Office and Data Provisioning Operations teams when required.
• Ensure dataset preparation follows agreed data governance principles and avoids unintended modification of protected datasets.
• Author and maintain computational pathology analysis plans and reports, including Data Collection Plans for model development.

Cross-Functional Collaboration

• Work closely with program managers, computational pathology biomarker leads, pathologists, machine learning engineers, and data scientists to ensure effective use of data resources.
• Communicate clearly about data readiness, limitations, and uncertainties to support scientific and technical decision-making.

Operational Excellence and Continuous Improvement

• Identify opportunities to improve data workflows, tooling, and operational processes within computational pathology.
• Stay informed about emerging data management practices, digital pathology technologies, and AI data standards.

Desired Profile

Education

• Bachelor’s or Master’s degree in data science, bioinformatics, biome