Applied Data Scientist — Knowledge Graphs & AI Research

Peraton; Red Bank (NJ); On-site; USD 146,000 - 234,000; Full time

Job summary

Peraton in Red Bank, NJ is seeking a Data Scientist to build and curate knowledge bases for a generative AI/ML research effort and to develop data and knowledge extraction pipelines for models.

The ideal candidate will implement pipelines for extracting, structuring, and validating design knowledge into knowledge graphs and ontologies, delivering reproducible datasets with provenance for the program data sharing schedule.

Qualifications

• Bachelor's degree or higher in a technical field listed.
• 5+ years of applied data science or data engineering experience, or MS with 3+ years.
• Strong Python proficiency with scientific stack and PyTorch.
• Hands-on experience with knowledge graphs, ontologies, and querying them (SPARQL/Cypher).
• Experience designing data schemas, metadata standards, and provenance.
• Solid statistical foundations with clear results explanations.
• Experience with SQL and a workflow/pipeline orchestration tool.
• Ability to produce clear data dictionaries and analysis reports.
• US Citizenship.

Responsibilities

• Design, build, and populate research knowledge bases with provenance tracking.
• Develop knowledge extraction pipelines (rule-based, statistical, ML) to structured content.
• Engineer metadata and packaging for program datasets to ensure reproducibility.
• Perform exploratory analysis and build dashboards to show model performance against objectives.
• Contribute data and analyses to reviews and coordinate with subcontractors.

Skills

Python

NumPy

pandas

SciPy

scikit-learn

PyTorch

SQL

Knowledge graphs

Data visualization

Graph querying

Education

Bachelor's degree or higher in CS/Math/Stats/EE

MS or PhD preferred

Tools

Neo4j

Cypher

SPARQL

RDF/OWL

Job description

Peraton in Red Bank, NJ is seeking a Data Scientist to build and curate knowledge bases for a generative AI/ML research effort and to develop data and knowledge extraction pipelines for models.

The ideal candidate will implement pipelines for extracting, structuring, and validating design knowledge into knowledge graphs and ontologies, delivering reproducible datasets with provenance for the program data sharing schedule.