Our client, a leading Life Sciences company, is hiring a Scientific Data Architect.

Work Location: Middlesex County, Massachusetts

Summary:

The Scientific Data Architect will be a critical team member in a unique partnership to industrialize Scientific AI. This role involves engaging directly with customers onsite up to 4-5 days per week in the Boston Region, building strong relationships, deeply understanding their scientific data challenges and requirements, and accelerating solutions.

Responsibilities:

• Design and implement extensible, reusable data models that efficiently capture and organize scientific data for scientific use cases, ensuring scalability and future adaptability
• Translate scientific data workflows into robust solutions leveraging the Tetra Data Platform
• Own, scope, prototype, and implement solutions including:
  • Data model design (tabular & JSON)
  • Python-based parser development
  • Lab software (e.g., ELN/LIMS) integration via APIs
  • Data visualization and app development in Python (using app frameworks like Streamlit and plotting tools like holoviews and Plotly)
  • Collaborate with Scientific Business Analysts (SBAs), customer scientists and applied AI engineers to develop and deploy models (ML, AI, mechanistic, statistical, hybrid)
  • Programmatically interrogating proprietary instrument output files
• Dynamically iterate with scientific end users and technical stakeholders to rapidly drive solution development and adoption through regular demos and meetings
• Proactively communicate implementation progress and deliver demos to customer stakeholders
• Collaborate with the product team to build and prioritize our roadmap by understanding customers' pain points within and outside Tetra Data Platform
• Rapidly learn new technologies (e.g., new AWS services or scientific analysis applications) to develop and troubleshoot use cases

Qualifications:

• PhD with 7+ years / Masters with 10+ years of industry experience in life sciences with extensive domain knowledge in drug discovery (target ID through lead optimization), preclinical development, CMC (all drug modalities), or product quality testing
• Proven track record of defining, designing, prototyping, and implementing productized AI/ML-driven use cases in cloud environments
• Collaborated with cross-functional teams, including product managers, software engineers, and scientific stakeholders
• Performed extensive exploratory data analysis and workflow optimization to enable scientific outcomes not previously possible
• Engaged diverse audiences, from scientists to executive stakeholders using excellent communication and storytelling abilities
• Advised scientists in a consulting capacity to further research, development, and quality testing outcomes
• Experience designing and implementing extensible, reusable data models for scientific use cases
• Experience with Python-based parser development and data visualization/app development (e.g., Streamlit, holoviews, Plotly)
• Experience integrating lab software (e.g., ELN/LIMS) via APIs
• Ability to collaborate with scientific and technical stakeholders to develop and deploy models (ML, AI, mechanistic, statistical, hybrid)
• Ability to rapidly learn new technologies and troubleshoot use cases

Published Category: Technology & Data & AI Solutions