Global Head of Technology & Architecture in Biomedical Research; BR
Listed on 2026-06-11
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IT/Tech
AI Engineer (Applied/Software), Data Scientist, Data Science Manager, Data Analyst
Location:
Basel Switzerland, 3 days/week in office
The Role
The Global Head of Technology & Architecture in Biomedical Research (BR) will spearhead our information technology strategy in drug discovery and early clinical development, ensuring our technologies are aligned with our objectives while promoting unity and cooperation with key internal and external partners. The role is focused on transforming our global labs into high-throughput, AI‑orchestrated environments by integrating ‘dry lab’ hypothesis generation with ‘wet lab’ robotic validation to build the ‘Self‑Driving Lab’ of the future.
The leader will be a member of the Biomedical Research Data & Digital Leadership Team and a matrixed member of the Data, Digital, and IT (DDIT) leadership team representing the needs of Biomedical Research. The position reports to the Chief Data & Digital Officer, , and works closely with the Data and Digital leadership team.
We are seeking an entrepreneurial and technically strong Global Head of Technology & Architecture to lead the strategy, design, and evolution of next‑generation Data & AI platforms and the lab of the future that supports our global biomedical research organization.
Key Responsibilities- Deliver top‑tier research informatics to expedite the discovery of new treatments using data & digital technology and enhance the user experience of our researchers.
- Ensure the evolution of ’s technology capabilities, underlying architecture, and drive make‑or‑buy decisions for software products and architecture in close alignment with DDIT, delivering harmonized, scalable, end‑to‑end approaches across projects.
- Lead the strategy, design, and evolution of next‑generation Data & AI platforms supporting a global biomedical research organization, define a multi‑year vision and roadmap for the Research Data & AI platforms, and align with scientific priorities and the digital AI strategy.
- Contribute to the creation of a global digital strategy for ’s AI‑driven drug discovery using Data Science, (Gen) AI, and ML, and participate in key strategic cross‑divisional data and digital projects to ensure seamless compatibility of data systems across Novartis Research‑Development and Commercial units.
- Drive the lab of the future initiative from an informatics perspective with lab equipment and automation experts from other functions.
- Collaborate closely with Novartis DDIT and the Data Science community to devise and execute the digital and AI strategy for , ensuring is a fully AI‑enabled research organization.
- Evaluate emerging technologies (agentic workflows, vector databases, GPU infrastructure, lab digitalization technologies, quantum computing, etc.) and translate them into practical capabilities that drive research outcomes; lead platform architecture reviews and steer technology selection.
- Provide strategic input and vision on technologies to the President of Research and ’s scientific leadership, acting as an advisor to top management.
- Partner closely with DDIT functions to ensure enterprise solutions are preferentially deployed where possible and seek synergies; serve as a bridge between engineering, R&D scientists, data science, and IT security/compliance and infrastructure groups.
- Grow and lead a diverse, inclusive, high‑performing team, instill a collaborative, enterprise mindset, and develop and mentor a high‑performing, diverse team of platform engineers, software engineers, domain specialists, architects, and lab technologists.
- PhD or master’s degree in computer science, data science, AI, computational biology, cheminformatics, or a related discipline. A background that spans both technology and life sciences is strongly preferred.
- 12+ years of experience in platform engineering, architecture, infrastructure, data engineering, cloud architecture, research lab technologies or ML infrastructure; with 8+ years in leadership roles.
- Strong understanding of modern AI/ML workflows, including LLMs, model training/serving, monitoring, MLOps, and GPU infrastructure.
- Expertise with cloud‑native architectures, microservices, APIs, Kubernetes, IaC, observability tools, and…
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