Scientific Business Analyst, Scientific AI- Boston job opportunity at TetraScience.

Home Jobs In United States Of America Tag: Scientific AI- Boston Scientific Business Analyst, Scientific AI- Boston position at TetraScience


Date2026-02-17T20:48:10.361Z bot
TetraScience Scientific Business Analyst, Scientific AI- Boston
Experience: Highly Experienced
Pattern: full-time
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Scientific AI- Boston

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loacation Cambridge, United States Of America
loacation Cambridge....United States Of America
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About TetraScience TetraScience is the Scientific Data and AI Company building Tetra OS, the operating system for scientific intelligence. We help the world’s leading life sciences firms turn fragmented scientific data into AI-native assets and scientific workflows that accelerate discovery, development, and manufacturing. TetraScience’s growing ecosystem of strategic partners includes NVIDIA, Databricks, Thermo Fisher Scientific, Snowflake, Google, and Microsoft. In connection with your candidacy, you will be asked to carefully review “ The Tetra Way ,” authored by our CEO, Patrick Grady; it is impossible to overstate the importance of this document, and you should take it literally as you decide whether our mission, culture, and expectations are right for you. Who You Are  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. What You Have Done PhD with 15+ years of industry experience in life sciences, preferably across pharma, biotech, or health tech, with deep domain expertise in discovery, preclinical, CMC, and/or Quality. Extensive hands-on experience or direct oversight in one or more of the following areas: high throughput screening, preclinical toxicology, materials engineering, analytical development, drug substance (DS) synthesis and manufacturing. Delivered requirements for AI/ML-driven solutions in operational or productized environments that improved efficiency, reduced cost, and enhanced data utilization. Extensive hands-on experience with scientific data workflows and lab automation; exposure to FAIR principles and modern data architecture is a plus. Strong coding or scripting background (e.g., Python, Nextflow, AWS, SDKs) and familiarity with scientific tools, databases, and ontologies is preferred. Exceptional communication and storytelling ability to engage technical and executive stakeholders. Prior experience in customer-facing, consulting, or commercial-scientific interface roles. What You Will Do  You will be a critical team member in a unique partnership to industrialize Scientific AI. As such, you will engage directly with customers onsite up to 4-5 days per week in the Boston Region 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.

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