Work Location: Middlesex County, Massachusetts

Summary:

Seeking a Scientific Data Architect to drive the development and implementation of AI-native scientific data solutions. This role focuses on transforming complex scientific data into actionable outcomes, collaborating with cross-functional teams, and delivering innovative data models and applications for the life sciences sector.

Responsibilities:

• 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 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 a variety of models (ML, AI, mechanistic, statistical, hybrid).
• Iterate dynamically with end users and technical stakeholders to drive rapid solution development and adoption through regular demos and meetings.
• Communicate implementation progress proactively and deliver solution demonstrations to stakeholders.
• Work with product teams to prioritize the roadmap by identifying and addressing customer pain points, while rapidly learning and applying new technologies as needed.

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.
• 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 both scientific and executive audiences.
• Consulting experience advising scientists to advance research, development, and quality testing outcomes.
• Hands-on experience with Python for parser development, data visualization (e.g., Streamlit, holoviews, Plotly), and lab software integration via APIs.
• Demonstrated 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 end users.

Published Category: Technology & Data & AI Solutions