Scientific Data Architect

Seeking a Scientific Data Architect to drive the design and implementation of advanced scientific data solutions. This role focuses on transforming complex scientific data into actionable insights, collaborating with cross-functional teams, and leveraging AI/ML methodologies to accelerate scientific outcomes. The ideal candidate is a proactive problem-solver with deep experience in life sciences, data modeling, and cloud-based product development.

• Engage directly with scientific stakeholders to understand data challenges and requirements, building strong relationships and accelerating tailored solutions.
• Design and implement scalable, reusable data models to efficiently organize scientific data for diverse use cases.
• Translate scientific workflows into robust, cloud-based solutions using advanced data platforms and tools.
• Prototype and implement solutions including data model design (tabular & JSON), Python-based parser development, and lab software integration via APIs.
• Develop data visualization and applications in Python, utilizing frameworks such as Streamlit and plotting tools like holoviews and Plotly.
• Collaborate with business analysts, scientists, and AI engineers to develop and deploy machine learning, AI, mechanistic, statistical, and hybrid models.
• Iterate dynamically with end users and technical stakeholders, driving solution adoption through regular demos, meetings, and proactive communication.
• Rapidly learn and apply new technologies to troubleshoot and develop scientific use cases, while contributing to product roadmap prioritization based on user feedback.

• PhD with 7+ years or Master’s with 10+ years of industry experience in life sciences, with extensive domain knowledge in drug discovery, preclinical development, CMC, or product quality testing.
• Proven 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 to enable novel scientific outcomes.
• Excellent communication and storytelling skills, with the ability to engage audiences ranging from scientists to executive stakeholders.
• Consulting experience advising scientists to advance research, development, and quality testing outcomes.
• Hands‑on experience with Python, data modeling (tabular & JSON), API integration, and scientific application development.
• Demonstrated ability to rapidly learn new tools, technologies, and scientific domains.
• Strong ownership mentality and a track record of building extensible data models and applications for scientific end users.