Data & Semantic Model Architect

🕒 vor 1 Monat

🇺🇸 Vereinigte Staaten – Remote

⏰ Vollzeit

🟠 Senior

🔴 Experte

🏛️ Architekt

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TetraScience

WebsiteLinkedInAlle Stellen

51 - 200 Mitarbeiter

Gegründet 2015

🤖 Künstliche Intelligenz

🧬 Biotechnologie

☁️ SaaS

Artificial Intelligence • Biotechnology • SaaS

TetraScience ist ein Unternehmen, das sich der Umwandlung von Rohdaten aus der Wissenschaft in AI-nahe Datensätze für fortschrittliche wissenschaftliche Anwendungen widmet. Durch die enge Zusammenarbeit mit führenden biopharmazeutischen Unternehmen steigert TetraScience die Produktivität, beschleunigt Erkenntnisse und gewährleistet die Datenintegrität entlang der wissenschaftlichen Wertschöpfungskette. Ihre Plattform bietet Lösungen für das Datenmanagement der nächsten Generation im Labor, AI-gesteuerte wissenschaftliche Ergebnisse und die Einhaltung von Industriestandards. Als erstes Unternehmen, das eine speziell für die Wissenschaft entwickelte Daten- und AI-Cloud bereitstellt, ermöglicht TetraScience seinen Kunden, ihre Daten zu befreien, zu vereinheitlichen und zu transformieren, um traditionelle Datensilos zu überwinden und die wissenschaftliche Produktivität durch eine flexible, offene und kollaborative Infrastruktur zu steigern.

Beschreibung

• 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.

🎯 Anforderungen

• 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.