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Data Scientist V

Herndon, Virginia, United StatesOnsiteFull TimePosted 2 months agoVisa sponsorship available

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Role summary

Byte Systems is seeking an Ontologically Aware Data Scientist to design, build, and operationalize knowledge-centric AI/ML systems. This role requires deep expertise in ontology engineering (RDF/OWL/SHACL/SKOS), graph data modeling (AnzoGraph), and SPARQL querying, combined with practical machine learning, NLP, and context engineering. Responsibilities include developing ontologies, knowledge graphs, and natural language to SPARQL interfaces. The ideal candidate will apply strong software engineering practices (Git, CI/CD) and possess proficiency in Python. This role is crucial for enabling organizations to communicate using shared ontologies and supporting advanced analytics.

Overview:

Byte Systems, a subsidiary of VTG, is seeking a Ontologically Aware Data Scientist to design, build, and operationalize knowledge centric AI/ML systems that integrate ontology engineering, semantic data modeling, and advanced analytics. This role blends deep experience with RDF/OWL/SHACL and graph data models with practical machine learning, natural language processing, and context engineering. You will develop semantic foundations and AI-driven tooling that enable multiple organizations to communicate using shared ontologies and that support natural language to SPARQL interfaces with reduced hallucination.

Responsibilities:

Responsibilities

  • Develop and maintain logical, semantically rich, extensible ontologies (RDF/OWL/SKOS) aligned to foundational/top level ontologies (BFO, DOLCE, CCO, OBO Foundry).
  • Build and evolve canonical vocabularies that unify heterogeneous datasets across organizational boundaries.
  • Design, implement, and maintain knowledge graphs and semantic data models using AnzoGraph including modeling tradeoffs, query optimization, and production operations.
  • Create SHACL validation layers; run regular quality checks to ensure consistency, backward compatibility, and schema integrity.
  • Write performant SPARQL queries; develop mapping views, CONSTRUCT transformations, and federated semantic integrations.
  • Develop hybrid ontology–GenAI capabilities, including natural language to SPARQL pipelines with context engineering frameworks (e.g., DSPy) to reduce hallucination and improve schema grounding.
  • Apply machine learning techniques—classification, embedding based retrieval, entity resolution, and model fine tuning—to support ontology evolution, reification, and inference.
  • Partner with cross functional teams (Data Engineering, Developers, content owners) to understand information needs and deliver semantic/analytical solutions.
  • Produce clear, accessible technical documentation; deliver onboarding and training for both technical and non technical users.
  • Apply strong software engineering discipline to semantic assets: version control, code review, automated SHACL/dbt tests, CI/CD pipeline alignment.
  • Support both new development and ongoing operations/maintenance across unclassified and classified environments.

Qualifications:

Required Qualifications

  • A bachelor’s degree and 14 years of relevant experience OR a Master’s degree with 12 years of relevant experience OR A PhD with 9 years of relevant experience
  • Prefer an advanced degree in Philosophy, Computer Science, Information Science, or a quantitative field, or equivalent experience.
  • Deep proficiency in ontology languages (RDF, OWL, SHACL) and vocabularies (SKOS).
  • Strong SPARQL skills; experience with semantic ETL, query optimization, and graph reasoning.
  • Proficiency in Python (plus R/Java as needed) for data science, graph processing, and ML model development.
  • Strong engineering practices: Git, PR workflow, testing/validation, CI/CD.
  • Active TS adjudication with ability to obtain the SCI/CI Poly.

Desired Qualifications

  • Hands-on experience with ontology/graph platforms such as Protégé, AnzoGraph, Stardog, PoolParty, or Semaphore.
  • Experience integrating disparate datasets into a unified semantic model and writing effective production-grade queries.
  • Familiarity with upper ontologies (BFO, DOLCE, Common Core Ontologies, OBO Foundry).
  • Knowledge of machine learning, including supervised/unsupervised methods, embeddings, and model fine tuning.
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