Work Location: Middlesex County, Massachusetts

Summary:

The Scientific Data Architect will play a pivotal role in advancing scientific AI by engaging directly with clients to understand and solve complex scientific data challenges. This position involves designing scalable data models, developing robust solutions, and collaborating with cross-functional teams to accelerate scientific outcomes.

Responsibilities:

• Engage with clients onsite to build relationships, understand scientific data challenges, and accelerate tailored solutions.
• Design and implement scalable, reusable data models to efficiently 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 applications.
• Collaborate with scientific analysts, engineers, and stakeholders to develop and deploy AI, ML, and statistical models.
• Iterate dynamically with end users and technical teams to drive rapid solution development and adoption through regular demonstrations and meetings.
• Communicate progress proactively and deliver solution demos to stakeholders to ensure alignment and transparency.
• Work closely with product teams to prioritize development roadmaps and rapidly learn new technologies to support evolving scientific needs.

Qualifications:

• PhD with 7+ years or Master’s with 10+ years of industry experience in life sciences, with 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 to enable novel scientific outcomes.
• Excellent communication and storytelling skills, with the ability to engage audiences ranging from scientists to executives.
• Consulting experience advising scientists to advance research, development, and quality testing.
• Proficiency in Python for parser development, data visualization (e.g., Streamlit, holoviews, Plotly), and lab software integration via APIs.
• Ability to rapidly learn and apply new technologies, such as AWS services and scientific analysis applications.

Published Category: Technology & Data & AI Solutions