Data & Semantic Model Architect

🕒 May 11

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🔴 Lead

🏛️ Architect

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TetraScience

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2015

🤖 Artificial Intelligence

🧬 Biotechnology

☁️ SaaS

Artificial Intelligence • Biotechnology • SaaS

TetraScience is a company dedicated to transforming raw scientific data into AI-native datasets for advanced scientific applications. By collaborating closely with leading biopharmaceutical companies, TetraScience enhances productivity, accelerates insights, and ensures data integrity across the scientific value chain. Their platform offers solutions for next-generation lab data management, AI-driven scientific outcomes, and compliance with industry standards. As the first company to provide a data and AI cloud built specifically for science, TetraScience enables its clients to liberate, unify, and transform their data, overcoming traditional data silos and boosting scientific productivity by providing a flexible, open, and collaborative infrastructure.

📋 Description

• Architect the Exchange Layer: Design and own the Common Data Models (CDMs) that serve as the universal language for scientific data across our customer base. Move the platform from bespoke, one-off mappings to a standardized "exchange layer" that ensures interoperability. • Empower Forward Deployed Engineering: Create the data contracts and standardized definitions that FDEs rely on. Your models will be the toolkit that allows them to deploy faster and with higher confidence, knowing they are building on a stable, consistent semantic foundation. • Standardization vs. Flexibility: Strike the strategic balance between rigid global standards (for cross-customer exchange) and local flexibility. Define the core "immutable" aspects of the model versus where extension is permitted. • Translate high-level business goals (e.g., "accelerate time-to-insight for biologics") into concrete data modeling strategies. Ensure our semantic roadmap directly supports the scientific questions our customers—and our internal teams—need to answer. • Roll up your sleeves to design and implement complex ontologies and taxonomies. Model intricate scientific relationships (e.g., linking a "Cell Line" in an ELN to "Flow Cytometry Results") with precision. • Work directly with Engineering to architect the software systems that consume these models. Ensure that the "perfect" ontology does not break query performance or system scalability. • Establish the "rules of the road" for data quality and consistency. Define how data contracts are versioned, enforced, and evolved, ensuring that downstream consumers (AI teams, FDEs, Scientists) can trust the data structure. • Partner with Scientific Business Analysts to decode the complexity of biopharma R&D. Turn ambiguous scientific requirements into rigorous, machine-readable data structures. • Architect models that ensure our data is FAIR (Findable, Accessible, Interoperable, Reusable) and ready for downstream AI/ML applications.

🎯 Requirements

• 7+ years of experience in data architecture, informatics, or technical product leadership, specifically within life sciences, healthcare, manufacturing technology or the ability to demonstrate complex, multidomain unification of data models & semantic layers. • CDM Framework Expertise: Direct, hands-on experience implementing and extending Common Data Model frameworks such as HL7 FHIR, OMOP (OHDSI), Allotrope, or CDISC. You should know the strengths and limitations of each for biopharma R&D. • Terminology & Standardization: Proven mastery in standardizing messy, heterogeneous data using both standard vocabularies (such as terminology standards & ontologies) as well as proprietary or custom vocabularies. You must have experience semantically curating (semantic mapping & aggregation; ie value set creation) between and across vocabularies as well as discrete instance data. • Platform & Exchange Experience: Experience building data platforms where standardization and reusability were key value drivers. You have likely built models that serve as an exchange layer across multiple customers. • Technical Background: Strong proficiency in software development concepts; you should be comfortable reading code, understanding API contracts, and discussing database internals.