# Director, Translational Data Enablement (m/f/d) at AstraZeneca

AstraZeneca is hiring a Director, Translational Data Enablement (m/f/d) in Munich, Germany. Level rates it Works on AI ●●●○; you can [apply on AstraZeneca](https://jobsbylevel.com/go/36d35379-0fb2-450c-82ec-f118139b35a8?utm_medium=ai-agent).

AI Level 3, AI centrality 65 out of 100. AZ Computational Pathology GmbH - Munich.

## Details

- Company: [AstraZeneca](https://jobsbylevel.com/companies/astrazeneca)
- AI level: AI Level 3 (score 65 out of 100)
- Location: AZ Computational Pathology GmbH - Munich
- Posted: September 14, 2026
- Apply: https://jobsbylevel.com/go/36d35379-0fb2-450c-82ec-f118139b35a8?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=director-translational-data-enablement-m-f-d-at-astrazeneca-2cbc27

## Prepare for this job

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## Description

Director, Translational Data Enablement (m/f/d) About AstraZeneca AstraZeneca is a global, science-led, patient-focused biopharmaceutical company that focuses on the discovery, development and commercialization 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 can feel comfortable to be themselves - a value that is at the core of AstraZeneca's priorities. To help you maintain your best self, here's a sneak peek into some of the things we provide: after-work events, lunch & learns, a spacious and sustainable office working environment, events, family and childcare support and of course the Alps around the corner for hiking, biking and skiing. We're building a connected, end-to-end Enterprise AI engine - uniting data foundations, AI technology, process reinvention, and business-facing AI to accelerate results across the whole value chain. Success depends on being exceptional connectors: you'll actively leverage existing capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation. If you thrive in high-collaboration environments where your role is to turn complex, cross-functional problems into reusable, enterprise-wide capabilities - and where the measure of success is adoption and scale, not just innovation - you'll have the platform (and sponsorship) to make it real. Own the transformation of translational and biomarker data into AI-ready, standardized data products that accelerate drug discovery and development. The role sits at the critical intersection of science teams (who generate and use the data), technology teams (who build platforms and automation), and peer data leadership across clinical trial submission and preclinical/discovery domains. Primary mandate: deliver high-quality, reusable data products while building capabilities to enable a AI ready and FAIR end to end data flow. Lead a 12–15 person distributed team and coordinate cross-functionally with peer Directors to ensure enterprise-wide data coherence and strategy alignment. Key responsibilities: Science Enablement & Delivery Own delivery of analysis-ready datasets to science teams, enabling precision medicine, biomarker discovery, and hypothesis validation Work with science stakeholders to understand analytics needs and shape data standards accordingly Create data catalogs, metadata standards, and usage guidelines; establish feedback mechanisms for continuous improvement Standards & Data Product Strategy Define FAIR-compliant standards for translational/biomarker data (omics, imaging, proteomics, etc.). Establish quality frameworks and SLAs aligned to regulatory, AI/ML, and precision medicine use cases Build semantic schemas and harmonization layers enabling integration of data from diverse sources (labs, vendors, partners) into reusable, consumable data products Platform & Technology Define technical requirements for translational data workflows (ingestion, validation, harmonization, delivery APIs) Lead automation initiatives to reduce manual curation (e.g., schema-driven harmonization, intelligent quality assurance). Measure efficiency gains Ensure integration with enterprise systems (clinical data lakes, AI/ML platforms) Pilot new technologies (agentic AI, ML-driven quality assurance) at scale Team Leadership & Cross-Domain Coordination Recruit, mentor, and scale a 12–15 person distributed team of data stewards and engineers responsible for data curation, validation, and delivery Partner with other team leads on shared deliveries, leveraging synergies and cont. increasing efficiency Strategic Leadership Define multi-year roadmap for expanding translational/biomarker data

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