As a Scientific Data Architect, you will lead the design and implementation of advanced data solutions that power innovation in biopharmaceutical research and development. Your work will focus on creating scalable, AI-driven applications in cloud environments that enable breakthroughs across drug discovery, preclinical development, and product quality.
Key Responsibilities
- Design and develop technical solutions for complex scientific data challenges in biopharma R&D
- Prototype and demonstrate AI/ML use cases to accelerate product delivery and user adoption
- Partner with scientists, product teams, and engineers to translate domain knowledge into functional applications
- Build reusable, extensible data models that enhance data integration and analytical capabilities
- Conduct in-depth data exploration and optimize workflows to unlock new scientific insights
- Communicate technical concepts effectively to both technical and non-technical stakeholders
- Provide expert guidance to scientific teams to improve research, development, and testing outcomes
Qualifications
- PhD with 4+ years or Master’s with 8+ years of relevant industry experience in life sciences
- Deep understanding of drug discovery processes, preclinical development, CMC, or product quality testing
- Proven experience in delivering AI/ML-driven solutions in cloud-based environments
- History of building data applications that deliver measurable value in biopharma contexts
- Strong record of cross-functional collaboration across scientific and technical teams
- Exceptional communication skills with the ability to tell compelling data stories
- Commitment to extreme ownership—driving initiatives from concept to impact
Environment & Culture
This role operates in a fast-moving, product-focused environment where learning and execution are prioritized. You’ll work on-site in either Darmstadt or Düsseldorf, embedded in a team that values technical rigor, collaboration, and continuous improvement. The culture emphasizes hands-on problem solving, deep domain engagement, and alignment with core principles of accountability and innovation.
Technologies you’ll work with include AI/ML frameworks, cloud platforms, and scientific data integration systems. You’ll collaborate with leaders in computing and AI infrastructure and contribute to next-generation lab data solutions that are shaping the future of scientific research.
