Knowledge Graph Lead - Precision Healthcare Tech
Curb, Oklahoma City, OK, United States
Knowledge Graph Lead, Data Science
Build the biological intelligence layer behind a next generation Digital Twin.
We are seeking a technically rigorous and visionary Knowledge Graph leader to design and scale the core intelligence system that unifies multimodal biomedical data into a living, inference ready platform. This role sits at the intersection of data engineering, computational biology, and applied machine learning, with a mandate to transform fragmented biological and clinical information into a cohesive, queryable representation of human health and disease.
You will create the structural backbone that enables advanced modeling, simulation, and personalized insights. This is not an academic exercise. It is a production critical system that must perform, evolve, and explain itself.
Your Mission
Design and operationalize a large scale Knowledge Graph that models human biology, disease progression, diagnostics, and therapeutic pathways. The graph will power a Digital Twin platform that integrates molecular, clinical, and longitudinal data into a unified computational framework.
What You Will Lead
Architect the Biological Graph
Design a scalable graph architecture representing genes, proteins, pathways, phenotypes, biomarkers, clinical events, interventions, and outcomes.
Model complex biological relationships and causal hypotheses in a way that supports reasoning, simulation, and downstream machine learning.
Balance scientific fidelity with computational performance and system maintainability.
Integrate Multi Omics and Clinical Data
Ingest and harmonize heterogeneous data streams including genomics, transcriptomics, proteomics, metabolomics, microbiome profiles, imaging metadata, and structured and unstructured clinical records.
Design normalization and entity resolution strategies that reconcile noisy real world datasets into consistent graph entities.
Implement robust data validation, lineage tracking, and audit trails suitable for biomedical and clinical contexts.
Define Ontologies and Semantic Frameworks
Develop domain specific ontologies, schemas, and relationship rules that enable biological and medical inference.
Align internal models with established standards where appropriate, while extending them to support novel use cases.
Ensure the graph is both interpretable for domain experts and flexible enough for evolving scientific knowledge.
Build Production Grade Graph Infrastructure
Implement and manage graph databases and related tooling such as Neo4j, TigerGraph, RDF frameworks, SPARQL endpoints, or comparable systems.
Create pipelines for continuous ingestion, versioning, and reproducibility.
Expose graph capabilities through well designed APIs and internal tooling to support Digital Twin modeling teams and product development.
Optimize for Real World Use
Ensure performance at scale across complex queries and inference workloads.
Prioritize explainability and traceability in graph relationships to support scientific credibility and regulatory considerations.
Design the system for extensibility as new data modalities, disease areas, and analytical approaches are introduced.
Your Background
Strong foundation in data engineering, graph systems, applied machine learning, or computational biology.
Hands on experience designing or operating Knowledge Graphs, semantic data models, or graph databases in production environments.
Comfort working with imperfect biomedical or clinical datasets and translating them into structured representations.
Proficiency in Python and at least one graph or query language such as SQL, Cypher, SPARQL, or an equivalent.
Ability to reason clearly about complex system design tradeoffs, balancing rigor, scalability, and product needs.
Why This Role Matters
You will shape the core intelligence layer of a Digital Twin platform that integrates molecular science and clinical reality. Your work will determine how data becomes knowledge, and how knowledge becomes actionable insight. If you are motivated by building foundational systems that advance precision health and computational medicine, this is an opportunity to define the architecture that makes it possible.
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Build the biological intelligence layer behind a next generation Digital Twin.
We are seeking a technically rigorous and visionary Knowledge Graph leader to design and scale the core intelligence system that unifies multimodal biomedical data into a living, inference ready platform. This role sits at the intersection of data engineering, computational biology, and applied machine learning, with a mandate to transform fragmented biological and clinical information into a cohesive, queryable representation of human health and disease.
You will create the structural backbone that enables advanced modeling, simulation, and personalized insights. This is not an academic exercise. It is a production critical system that must perform, evolve, and explain itself.
Your Mission
Design and operationalize a large scale Knowledge Graph that models human biology, disease progression, diagnostics, and therapeutic pathways. The graph will power a Digital Twin platform that integrates molecular, clinical, and longitudinal data into a unified computational framework.
What You Will Lead
Architect the Biological Graph
Design a scalable graph architecture representing genes, proteins, pathways, phenotypes, biomarkers, clinical events, interventions, and outcomes.
Model complex biological relationships and causal hypotheses in a way that supports reasoning, simulation, and downstream machine learning.
Balance scientific fidelity with computational performance and system maintainability.
Integrate Multi Omics and Clinical Data
Ingest and harmonize heterogeneous data streams including genomics, transcriptomics, proteomics, metabolomics, microbiome profiles, imaging metadata, and structured and unstructured clinical records.
Design normalization and entity resolution strategies that reconcile noisy real world datasets into consistent graph entities.
Implement robust data validation, lineage tracking, and audit trails suitable for biomedical and clinical contexts.
Define Ontologies and Semantic Frameworks
Develop domain specific ontologies, schemas, and relationship rules that enable biological and medical inference.
Align internal models with established standards where appropriate, while extending them to support novel use cases.
Ensure the graph is both interpretable for domain experts and flexible enough for evolving scientific knowledge.
Build Production Grade Graph Infrastructure
Implement and manage graph databases and related tooling such as Neo4j, TigerGraph, RDF frameworks, SPARQL endpoints, or comparable systems.
Create pipelines for continuous ingestion, versioning, and reproducibility.
Expose graph capabilities through well designed APIs and internal tooling to support Digital Twin modeling teams and product development.
Optimize for Real World Use
Ensure performance at scale across complex queries and inference workloads.
Prioritize explainability and traceability in graph relationships to support scientific credibility and regulatory considerations.
Design the system for extensibility as new data modalities, disease areas, and analytical approaches are introduced.
Your Background
Strong foundation in data engineering, graph systems, applied machine learning, or computational biology.
Hands on experience designing or operating Knowledge Graphs, semantic data models, or graph databases in production environments.
Comfort working with imperfect biomedical or clinical datasets and translating them into structured representations.
Proficiency in Python and at least one graph or query language such as SQL, Cypher, SPARQL, or an equivalent.
Ability to reason clearly about complex system design tradeoffs, balancing rigor, scalability, and product needs.
Why This Role Matters
You will shape the core intelligence layer of a Digital Twin platform that integrates molecular science and clinical reality. Your work will determine how data becomes knowledge, and how knowledge becomes actionable insight. If you are motivated by building foundational systems that advance precision health and computational medicine, this is an opportunity to define the architecture that makes it possible.
#J-18808-Ljbffr