The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading life sciences organization is seeking a Senior Scientific Data Architect to advance AI-native scientific data solutions. The organization focuses on biopharma R&D, providing innovative platforms and analytics solutions that support drug discovery, preclinical development, and product quality testing. The role emphasizes transforming complex scientific data into actionable insights for research and product development.
Summary
The Senior Scientific Data Architect will design and implement scalable, AI-driven scientific data solutions. This role requires collaborating with scientists, product managers, and engineers to translate complex scientific workflows into robust, reusable data models, enabling advanced analytics and AI/ML applications in the life sciences domain.
Responsibilities
Engage directly with scientific end users to understand data challenges and requirements, building strong relationships to accelerate tailored solutions
Design and implement scalable, reusable data models to organize scientific data for diverse use cases
Translate scientific workflows into robust solutions using advanced data platforms and tools
Prototype and implement solutions including data model design, parser development, lab software integration, and data visualization/app development in Python
Collaborate with analysts, scientists, and AI engineers to develop and deploy models including ML, AI, mechanistic, statistical, and hybrid approaches
Iterate dynamically with stakeholders through regular demos and meetings to drive solution adoption and continuous improvement
Communicate progress proactively and deliver solution demonstrations to stakeholders
Work with product teams to prioritize development roadmaps and rapidly learn new technologies to support evolving scientific use cases
Qualifications
PhD with 7+ years or Master’s with 10+ years of industry experience in life sciences
Deep domain knowledge in drug discovery, preclinical development, CMC, or product quality testing
Demonstrated experience defining, designing, prototyping, and implementing AI/ML-driven use cases in cloud environments
Strong background collaborating with cross-functional teams including product managers, engineers, and scientific stakeholders
Expertise in exploratory data analysis and workflow optimization for scientific outcomes
Excellent communication and storytelling skills, engaging both scientific and executive audiences
Consulting experience advising scientists to advance research, development, and quality testing
Hands-on experience with Python, data model design (tabular & JSON), parser development, API integrations, and data visualization frameworks (e.g., Streamlit, Holoviews, Plotly)
Ability to rapidly learn new tools, technologies, and scientific domains
Strong sense of ownership, self-discipline, and determination in building extensible data models and applications for scientific users
Disclaimer
SpringCube curates tech job listings from various company websites to support tech professionals in globally.
1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality. 2. No Client Relationship: This company is not a client of SpringCube unless stated. 3. To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job. 4. No Liability: SpringCube is not liable for inaccuracies.
Save
