Data Scientist

Peraton Overview Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world’s leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace.

The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees do the can’t be done by solving the most daunting challenges facing our customers. Visit  to learn how we’re keeping people around the world safe and secure.

Responsibilities We are seeking a Data Scientist to build and curate the knowledge base for a generative AI/ML research effort, and to develop the data and knowledge extraction pipelines for machine learning models. The role is responsible for extracting, structuring, and validating design knowledge into knowledge graphs and ontologies; for engineering the metadata, annotation, and provenance of program datasets; and for the analysis that turns results, laboratory measurements, and simulation output into actionable findings for the research team.

The ideal candidate is a strong applied data scientist with an interest in symbolic and structured representations of knowledge.

Experience with formal methods and domain-specific languages (DSLs) is desired but not required.

Key Responsibilities:

Design, build, and populate the research team’s effort knowledge bases, and maintain it under version control with provenance tracking

Develop knowledge extraction pipelines (rule-based, statistical, and ML-assisted) that convert unstructured and semi-structured sources into structured, queryable knowledge; establish quality metrics and validation procedures for extracted content

Engineer the metadata, annotation schema, and packaging for program datasets (synthetic, simulated, and real collections) so that datasets are reproducible, well documented, and deliverable on the program data sharing schedule

Perform exploratory and inferential analysis on training data, simulation output, and evaluation results; build dashboards and reports that show where generated waveforms succeed or fail against objectives, and feed findings back to AI model researchers and engineers

Contribute data and analysis sections to design reviews, monthly status reports, and dataset documentation; coordinate with academic subcontractors on shared data and knowledge resources

Qualifications

Required Qualifications:

Bachelor’s Degree or higher in Computer Science, Statistics, Mathematics, Electrical Engineering, or a related technical field5+ years of applied data science or data engineering experience (or MS with 3+ years) with a record of delivering data pipelines and analyses that other engineers and researchers depend on Strong Python proficiency including the scientific stack (Num Py, pandas, Sci Py, scikit-learn) and experience with at least one deep learning framework (PyTorch preferred)
Hands-on experience with knowledge graphs, ontologies, or structured knowledge representation (RDF/OWL, property graphs such as Neo4j, or equivalent) and with querying and validating them (SPARQL, Cypher, SHACL, or similar)
Experience designing data schemas, metadata standards, and annotation workflows for large scientific or engineering datasets, including data versioning and provenance

Solid statistical foundations: experimental design, hypothesis testing, uncertainty quantification, and the ability to explain results to technical…