Director, Translational Data Enablement

Location:

This role can be based in

Cambridge, UK , or

Munich, Germany . 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