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# Ontology and Data Science Engineer

- **Company:** Indigo Industries Inc
- **Location:** Arlington, VA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Systems Engineering, Cloud Computing, Computer Programming, Data Infrastructure, Data Structures, R (Programming Language), Graph Database, Python (Programming Language), Logical Data Models, Neo4j, Semantic Web, SPARQL, Systems Modeling Language, Systems Architecture, Apex Code, Pandas, Scikit Learn, Information Technology
- **Published:** July 10, 2026
- **Apply:** https://www.clearancejobs.com/jobs/9023234/ontology-and-data-science-engineer

## About the Role

Professional Experience:

 * Professional Experience: Mid-level professional (5-8+ years) with data science, systems engineering, or ontology development experience.
 * DoD Domain Context: Prior experience supporting DoD/DoW mission sets, processes, or data environments is a strong plus, though not a strict requirement.
 * Data Science & Modeling Proficiency: Strong proficiency in Statistical Software, data science programming (e.g., Python, Pandas, Scikit-learn, R), and SysML for MBSE efforts.
 * Semantic Web Expertise: Applied experience with semantic web technologies and ontology languages (e.g., OWL, RDF, SPARQL). Familiarity with graph databases (e.g., Neo4j, ArangoDB) and MBSE software (e.g., Cameo Systems Modeler) is highly desirable., * Education: Bachelor's or Master's degree in Data Science, Systems Engineering, Computer Science, Mathematics, or a related technical field.
 * Certifications: Relevant certifications in Data Science, MBSE (e.g., OCSMP), or Cloud infrastructure are preferred.

## Description

* Ontology Development & Integration: Design, develop, and maintain ontologies to structure complex data, ensuring alignment and integration with overarching Mission Engineering ontologies within the MEIA JCDE environment.
 * Analytical Modeling: Develop, test, and support advanced analytical and statistical models to assess the performance, gaps, and effectiveness of current and proposed systems and capabilities.
 * System Modeling: Utilize SysML to support MBSE efforts, mapping system architectures and behaviors to underlying data structures and ontologies.
 * Data Translation: Act as a bridge between systems engineers and data architects, translating mission requirements into logical data models and quantitative assessments.
 * Reporting & Visualization: Synthesize complex data science outputs and semantic queries into clear, actionable insights for mission stakeholders and decision-makers.Technical Liaison: Support the APEX by translating mission requirements into technical specifications for enterprise IT partners.


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