Shape the future of scientific data in biopharma R&D
As a Scientific Data Architect, you will lead the creation of advanced data frameworks that power artificial intelligence applications in life sciences. Your work will bridge deep scientific understanding with modern cloud technologies to unlock insights from experimental data across drug discovery and development.
What You'll Do
- Design and implement AI-native data sets tailored to scientific workflows in biopharma
- Develop next-generation data platforms that streamline lab data capture, integration, and analysis
- Translate complex research data into structured, reusable assets that drive machine learning applications
- Partner with scientists, engineers, and product teams to identify high-impact use cases and deliver technical solutions
- Conduct in-depth exploratory data analysis to uncover patterns and optimize research processes
- Prototype and demonstrate tools that accelerate scientific decision-making
- Advise research teams on data strategy, modeling, and integration best practices
- Communicate technical findings effectively to both technical and non-technical audiences
What We're Looking For
- PhD with at least 4 years or Master’s with 8+ years of industry experience in life sciences or related fields
- Deep expertise in drug discovery, preclinical development, CMC, or product quality testing
- Proven experience building AI/ML-driven solutions in cloud environments
- Solid background in creating scalable data models and applications for scientific end users
- Strong analytical skills with a track record of optimizing scientific workflows through data
- Exceptional communication abilities and a consulting mindset
- Commitment to ownership, continuous learning, and delivering measurable outcomes
- Alignment with core values of collaboration, speed, and product-centric thinking
Technology & Environment
You'll work with cutting-edge AI/ML tools and cloud platforms, focusing on scientific data modeling, integration, and workflow optimization. The role emphasizes rapid iteration, cross-disciplinary collaboration, and a product-oriented approach to solving real-world research challenges.
