
TetraScience offers a Scientific Data and AI Cloud designed to liberate, unify, and transform raw scientific data into AI-native data. This platform aims to enable Scientific AI by providing a…

TetraScience offers a Scientific Data and AI Cloud designed to liberate, unify, and transform raw scientific data into AI-native data. This platform aims to enable Scientific AI by providing a…
Company: TetraScience — scientific data and AI cloud for life‑sciences R&D
Headquarters / Founded: Boston; founded (original) 2014; rebuild noted from 2017; current founding year reported 2019 in one source
Product: Tetra Scientific Data and AI Cloud (Tetra OS) — vendor‑agnostic, AI‑native data platform
Recent round: Series B $80M announced April 15, 2021 (co‑led by Insight Partners and Alkeon Capital)
Used by many large pharma/biotech customers (company cites percent of top pharma customers)
Scientific R&D data management and enabling AI for discovery and research productivity in life sciences.
2019
Software Development
8,000,000
Series A included strategic investor Waters Corporation and investors Floodgate, First Round, Underscore VC, Founder Collective
80,000,000
Announced to accelerate rollout of the open R&D Data Cloud
“Investors include Insight Partners, Alkeon Capital, Waters Corporation, Floodgate, First Round Capital, Underscore VC, Founder Collective, and others”
TetraScience is the Scientific Data and AI company. We are catalyzing the Scientific AI revolution by designing and industrializing AI-native scientific data sets, which we bring to life in a growing suite of next gen lab data management solutions, scientific use cases, and AI-enabled outcomes.
TetraScience is the category leader in this vital new market, generating more revenue than all other companies in the aggregate. In the last year alone, the world’s dominant players in compute, cloud, data, and AI infrastructure have converged on TetraScience as the de facto standard, entering into co-innovation and go-to-market partnerships: Latest News and Announcements | TetraScience Newsroom:
In connection with your candidacy, you will be asked to carefully review the letter, authored directly by Patrick Grady, our co-founder and CEO. This letter is designed to assist you in better understanding whether TetraScience is the right fit for you from a values and ethos perspective.
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It is impossible to overstate the importance of this document and you are encouraged to take it literally and reflect on whether you are aligned with our unique approach to company and team building. If you join us, you will be expected to embody its contents each day.
You are a strategic, analytically minded professional with a passion for bridging scientific insights and cutting-edge technology. You thrive in environments where you can collaborate with scientists, product managers, and engineers to transform complex scientific data into actionable outcomes.
With deep domain knowledge in drug discovery/preclinical development, CMC, or Quality, you are skilled at uncovering innovative use cases that drive AI and machine learning applications. Your ability to engage with scientists and business leaders alike makes you a key player in maximizing the value of scientific data.
You will need to be a high clock speed and forward-thinking individual with a passion for developing requirements for complex solutions targeted to R&D and Quality personas inside of Life Sciences.
You will need to be a high clock-speed, forward-thinking individual with a passion for developing requirements for complex solutions targeted to R&D and Quality personas inside Life Sciences. You embody extreme ownership and have a demonstrated history of deriving maximum value from data through enrichment, analysis, and integration with AI and machine learning applications.
You should also be energized by regularly working onsite with customers. You thrive in dynamic, high-impact, face-to-face collaborative environments where you can build deep relationships and drive scientific transformation firsthand.
Customer Data Exploration: Investigate diverse customer datasets, identifying enrichment and AI-readiness opportunities.
Scientific Use Case Development: Collaborate with customers to define, iterate, and implement innovative scientific AI/ML use cases.
Stakeholder Engagement: Conduct onsite interviews and workshops to deeply understand customer challenges and data landscapes.
Data Analysis and Enrichment: Perform exploratory data analysis and define transformation workflows that enable scientific AI.
Workflow Documentation: Develop visual documentation including workflow diagrams, ERDs, and ontology definitions.
AI Model Evaluation: Provide practical scientific input on model output, with suggestions to improve real-world performance.
Customer Enablement: Deliver onsite demonstrations, conduct working sessions, and act as a trusted advisor in AI adoption.
Strategic Insight: Propose new directions, experiments, or platforms that can amplify scientific discovery and development.