Director, Translational Data Enablement

Location Munich, Bavaria, Germany Job ID R-256967 Date posted 22/07/2026

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 standardization across therapeutic areas and partners

Drive shift from reactive data cleanup to proactive "Shift Left" data generation

Present at industry forums; own P&L for translational data operations

Required experience & qualifications

PhD or master degree in bioinformatics, biomedical data science, molecular medicine, or related field

Published research or thought leadership on biomarker standardization, data harmonization, and data product build and delivery with 5+ years experience

Experience with leading a cross functional, global team including budget oversight, hiring, onboarding

Experience with FAIR data principles, semantic interoperability, or data standards in research contexts (GA4GH, MIAME, etc.)

Track record scaling data governance or data stewardship programs across multiple labs, studies, or organizations

Familiarity with agentic AI, machine learning, or LLM-driven automation in data/science workflows

Familiarity with biomarker platform companies (e.g., Guardant Health, Foundation Medicine, Tempus) or research consortia (e.g., NCI's SEQC, GTEx)

Ready to lead the transformation of healthcare through AI? Join us in building the platform that will power the next generation of life-changing medicines and make a meaningful impact on patients' lives worldwide.

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. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.

At AstraZeneca, we are driven by a shared purpose to make a difference in patients' lives through innovation and collaboration. Our dynamic environment encourages continuous learning and growth as we explore new technologies and challenge conventional approaches. By partnering across functions and leveraging our data capabilities, we empower our teams to achieve remarkable outcomes. Join us as we shape the future of healthcare and contribute to AstraZeneca's mission of delivering life-changing medicines.

#EAI

Date Posted

23-jul-2026

Closing Date

29-jul-2026

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.