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TetraScience

Cloud-native platform centralizing scientific data

Scientific Data Architect

Data ManagementConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • PhD with 4+ years or Master's with 8+ years of industry experience in life sciences, with extensive domain knowledge in drug discovery (target identification through lead optimization), preclinical development, chemistry, manufacturing, and controls (CMC) for all drug modalities, or product quality testing.
  • Have a proven track record of defining, designing, prototyping, and implementing productized AI- and machine-learning-driven use cases in cloud environments.
  • Have collaborated with cross-functional teams, including product managers, software engineers, and scientific stakeholders.
  • Have performed extensive exploratory data analysis and workflow optimization to enable previously unavailable scientific outcomes.
  • Have engaged scientists and executive stakeholders using communication and storytelling abilities.
  • Have advised scientists in a consulting capacity to further research, development, and quality testing outcomes.

Responsibilities

  • Engage directly with customers onsite, build strong relationships, deeply understand their scientific data challenges and requirements, and accelerate solutions; travel up to 25% of the time.
  • 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 using the Tetra Data Platform.
  • Own, scope, prototype, and implement solutions, including data model design in tabular and JSON formats; Python-based parser development; lab software integration through APIs; and data visualization and application development in Python using frameworks such as Streamlit and plotting tools such as holoviews and Plotly.
  • Collaborate with Scientific Business Analysts, customer scientists, and applied AI engineers to develop and deploy machine-learning, AI, mechanistic, statistical, and hybrid models.
  • Programmatically interrogate proprietary instrument output files.
  • Iterate with scientific end users and technical stakeholders through regular demos and meetings to drive solution development and adoption.
  • Communicate implementation progress proactively and deliver demos to customer stakeholders.
  • Collaborate with the product team to build and prioritize the roadmap by understanding customer pain points within and outside the Tetra Data Platform.
  • Learn new technologies, such as AWS services or scientific analysis applications, to develop and troubleshoot use cases.

About the Company

TetraScience

Cloud-native platform centralizing scientific data

TetraScience offers a cloud-based Scientific Data Cloud that helps biopharmaceutical teams collect, centralize, and harmonize scientific data from lab instruments, software, and apps. It connects data across R&D, QA/QC, and manufacturing so data workflows run faster and data is ready for AI/ML analyses. The platform is vendor-neutral and open, designed to work with any lab equipment or software, providing end-to-end data management across the biopharma value chain. Its goal is to improve scientific outcomes by turning scattered data into a unified, ready-to-use data foundation that accelerates data-driven discovery and production.

Company size
201-500
Company stage
Series B
Total funding
$117.2M
Headquarters
Boston, MA
Founded
2019

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