Eine komplette Bewerbung in einer Minute — maßgeschneiderter Lebenslauf und Anschreiben, versandbereit.
Roche is seeking a Clinical and Real World Evidence Digital Solution Lead to bridge business needs with data science and platform engineering. You will evaluate AI-ready data products, guide PoCs, and scale solutions across RW and clinical data ecosystems.
The role emphasizes GenAI, responsible data handling, and governance while delivering scalable, production-ready digital solutions that create business value and scientific impact.
At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters.
As a Clinical and Real World Evidence Digital Solution Lead (Technology Consultant), you possess a strong understanding of complex business and scientific workflows, data science workflows, AI/ML/GenAI technologies, and modern data platform capabilities including AI-ready data product requirements. You will support the identification, evaluation, development, and scaling of innovative data and AI capabilities across the real-world and clinical data ecosystem.
This role bridges business needs, data science expertise, evidence generation workflows, and platform engineering to ensure that new capabilities progress from idea and proof of concept into scalable, governed, and sustainable digital solutions.
Acting as a trusted technical and data science partner, you will contribute to PoCs, product evaluations, data product roadmaps, productisation of innovative solutions. You will help ensure that emerging capabilities are evaluated for business value, scientific relevance, technical feasibility, scalability, governance, cost-effectiveness, and fit with the broader RW&C data ecosystem.