You will have technical product ownership discipline (backlog management, sprint planning, release management, stakeholder translation) with genuine high throughput laboratory automation fluency; enough to shape the redesign of laboratory workflows into AI-first workflows alongside the business, converse credibly with DAR scientists across the DMTA lifecycle, and make sound technical trade-offs that respect the underlying scientific method rather than working from a purely IT lens. You will own the backlog, architecture direction, and delivery cadence for the development squads building an AI-native, integrated family of capabilities spanning the request, execution, and return of experimental data generated in external laboratories and through in vivo research, ensuring what gets built is technically robust, scalable, well-integrated with the broader Labs of Tomorrow ecosystem, and delivered in a way that enables the scientific and business value the Business Lead is accountable for.