Shape the future of scientific data in biopharma by designing intelligent, AI-native data architectures that power next-generation research. In this role, you will bridge advanced data science with real-world life sciences challenges, creating robust data models that enable faster, more accurate discovery and development decisions.
What You'll Do
Lead the design and implementation of scientific data frameworks tailored for AI and machine learning applications. Translate intricate biological and chemical data into structured, scalable datasets that drive actionable insights across drug discovery, preclinical development, and quality testing. Partner with scientists, engineers, and product teams to prototype and refine solutions that directly impact research outcomes.
- Develop and deploy data models in cloud environments that support AI/ML-driven use cases from target identification through lead optimization
- Perform in-depth exploratory analysis to uncover patterns, streamline workflows, and enhance data utility
- Advise scientific teams on data strategy, helping them extract maximum value from experimental and operational data
- Communicate technical concepts clearly to both technical and non-technical audiences, from lab scientists to executive stakeholders
- Champion best practices in data integration, modeling, and ownership across cross-functional initiatives
Who You Are
You hold an advanced degree in a life sciences discipline and bring extensive industry experience in biopharma R&D. Your background includes hands-on work with cloud-based AI/ML systems, where you've built scalable, product-grade data solutions. You thrive in collaborative environments, blending scientific rigor with technical innovation.
You operate with extreme ownership—driving projects from concept to impact without waiting for direction. You're constantly learning, adapting quickly to new tools and domains. Your work reflects a commitment to scientific excellence, clear communication, and practical problem solving.
This position is based in Darmstadt or Düsseldorf, with a focus on local collaboration and high-velocity execution.
