TetraScienceData & Semantic Model Architect
Who We Are
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 Tetra Way 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.
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.
The Role
The Data & Semantic Model Architect will serve as the technical and strategic anchor for the "Semantic Layer" and the Common Data Model (CDM) of the Tetra Scientific Data and AI Cloud. You are the rare individual who can "do it all"—bridging deep technical semantics, system architecture, and business outcomes.
Crucially, you will be the owner of the Common Model & Exchange Layer of our platform: a set of unified, reusable common data models that allow data to flow seamlessly across different customer environments while driving towards true Ontology. You will define the data contracts and consistent definitions that empower our Forward Deployed Scientific Data Engineers & Architects (FDEs) to deliver rapid, reliable integrations without reinventing the wheel for every deployment. You will ensure our models are not just academically sound, but serve as the robust foundation for scalable data exchange and scientific insight.
What You Will Do
1. Common Data Model & Exchange Strategy
- 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.
2. Semantic Architecture & Implementation
- The "Forest" – Business Alignment: 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.
- The "Trees" – Hands-on Modeling: 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.
- Software & Data Engineering Integration: 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.
3. Cross-Functional Leadership & Governance
- Data Contracts & Governance: 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.
- Scientific Translation: Partner with Scientific Business Analysts to decode the complexity of biopharma R&D. Turn ambiguous scientific requirements into rigorous, machine-readable data structures.
- Interoperability: Architect models that ensure our data is FAIR (Findable, Accessible, Interoperable, Reusable) and ready for downstream AI/ML applications.
Skills & Competencies
Common Data Model Expertise: Proven ability to design shared data models that serve as an exchange format between different systems or organizations. You understand the challenges of mapping heterogeneous source data into a single, unified target schema.
Data Contract Design: Experience defining and enforcing data contracts in a microservices or platform environment. You know how to create specifications that developers and FDEs can build against reliably.
Architectural Versatility: The ability to switch context effortlessly between high-level system design (software architecture) and low-level entity relationship modeling.
Semantic Fluency: Deep, hands-on expertise with semantic web standards (RDF, OWL, SHACL, SPARQL) and property graph concepts (LPG).
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.
Education: Bachelor's or Master’s +in a relevant field (e.g., Medical Informatics, Computer Science, Bioinformatics, Physics).