DATA Scientist - GRAPHS & AI in Lexington Park

Job Description Job Description

Jaster Engineering Technology (JET) Systems develops, maintains, and innovates systems for specific and common mission capabilities, combat aides, and surveillance systems. We provide solutions through cost‑effective, tailored strategies to meet mission requirements. JET is committed to working with government agencies and commercial partners to develop modern autonomous and operator‑interfaced control systems. The Data Scientist – Knowledge Graphs and AI supports the design, development, integration, and application of advanced data solutions that transform complex information into actionable knowledge.

This role combines statistical analysis, data engineering, ontology development, knowledge graph design, and artificial intelligence and machine learning methods to support customer missions and product development. The position reports to the Product Development Team Lead and collaborates with software engineers, data architects, analysts, subject‑matter experts, and customer stakeholders.

The Data Scientist – Knowledge Graphs and AI position supports a hybrid work environment, providing flexibility for in‑person and remote work based on program and customer needs. Candidates must reside within the St. Mary’s County/NAS Pax River commuting area and be available to report onsite as required. The role requires the ability to work within established data workflows and architectures while quickly adopting new concepts and contributing immediate value.

The employee must possess the ability to obtain and maintain a DoD Secret security clearance. Compensation and work schedule will be based on experience, program requirements, and company policy. As a valued team member, we provide the following employee benefits:
Health, dental, and vision insurance;
Life Insurance, Paid Time Off; and a 401(K) with employer matching.

• Design, develop, evaluate, and maintain data science solutions that support operational, analytical, and product development objectives.
• Apply statistical modeling, exploratory data analysis, machine learning, and other quantitative methods to identify patterns, relationships, risks, and opportunities within complex datasets.
• Develop and support data pipelines, transformation processes, and scalable data architectures for structured, semi‑structured, and unstructured information.
• Build, extend, and maintain ontologies, taxonomies, semantic data models, and controlled vocabularies using concepts and standards such as Web Ontology, Resource Description Framework, and linked data.
• Contribute to the design, implementation, and governance of knowledge graphs that connect data, concepts, relationships, and business or mission rules.
• Integrate artificial intelligence and machine learning capabilities with structured data, semantic models, and knowledge graph environments.
• Use Python, R, or comparable analytics tools to clean, analyze, model, visualize, and validate data.
• Query and manipulate structured and semi‑structured data using SQL, SPARQL, and other appropriate query or interfaces.
• Assess real‑world datasets for quality, completeness, consistency, lineage, usability, and fitness for intended analytical purposes.
• Collaborate with software developers, data engineers, analysts, and subject‑matter experts to align data products with established technical architectures and customer requirements.
• Develop technical documentation for data models, ontologies, analytical methods, assumptions, data transformations, model performance, and system interfaces.
• Communicate complex data science, semantic technology, and AI/ML concepts clearly to technical personnel, customers, and non‑technical stakeholders.
• Participate in solution design reviews, model validation, data governance, testing, demonstrations, and continuous improvement activities.
• Independently research emerging methods and technologies, identify practical applications, and recommend improvements beyond assigned tasks.

• Master’s degree in Data Science, Computer Science, Information Science, Applied Mathematics, Statistics, Engineering, or a closely related field.
• Foundational…