Data Scientist

Peraton

. Build and curate the knowledge base for a generative AI/ML research effort .

Posted 9/29/2026full-timeRed Bank • New Jersey • United StatesMid-LevelSenior💰 $146,000 - $234,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and curating knowledge bases for generative AI and machine learning, with strong capabilities in data extraction, validation, and analysis. Proficient in Python and familiar with deep learning frameworks, knowledge representation, and data versioning practices.

Highest-signal resume keywords

Python ProficiencyKnowledge Graphs ExperienceDeep Learning FrameworksData Versioning and ProvenanceStatistical Foundations

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Extraction PipelinesKnowledge GraphsMachine Learning ModelsStatistical AnalysisData ValidationSQLData SchemasMetadata StandardsAnnotation WorkflowsExperimental Design

Soft Skills

Clear DocumentationCollaboration

Tools & Technologies

NumPyPandasSciPyScikit-learnPyTorchSPARQLCypherAirflowPrefectNeo4j

Industry Keywords

Generative AIMachine LearningData ScienceData EngineeringOntologyRDFOWLProvenance TrackingVersion ControlScientific Datasets

Tech Stack

Tools & technologies

AirflowNeo4jNumpyPandasPythonPyTorchScikit-LearnSQL

About the role

Key responsibilities & impact

Build and curate the knowledge base for a generative AI/ML research effort
Develop data and knowledge extraction pipelines for machine learning models
Extract, structure, and validate design knowledge into knowledge graphs and ontologies
Engineer metadata, annotation, and provenance for program datasets
Analyze results, laboratory measurements, and simulation output to produce actionable findings
Design, build, populate, and maintain research knowledge bases under version control with provenance tracking
Develop rule-based, statistical, and ML-assisted pipelines that convert unstructured and semi-structured sources into structured, queryable knowledge
Establish quality metrics and validation procedures for extracted content
Engineer metadata, annotation schemas, and packaging for synthetic, simulated, and real datasets
Perform exploratory and inferential analysis on training data, simulation output, and evaluation results
Build dashboards and reports showing where generated waveforms succeed or fail against objectives
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

Requirements

What you’ll need

Bachelor’s Degree or higher in Computer Science, Statistics, Mathematics, Electrical Engineering, or a related technical field
5+ years of applied data science or data engineering experience, or MS with 3+ years
Strong Python proficiency including NumPy, pandas, SciPy, and scikit-learn
Experience with at least one deep learning framework, PyTorch preferred
Hands-on experience with knowledge graphs, ontologies, or structured knowledge representation, including RDF/OWL, Neo4j, or equivalent
Experience querying and validating knowledge graphs using SPARQL, Cypher, SHACL, or similar
Experience designing data schemas, metadata standards, and annotation workflows for large scientific or engineering datasets
Experience with data versioning and provenance
Solid statistical foundations including experimental design, hypothesis testing, and uncertainty quantification
Experience with SQL and at least one workflow or pipeline orchestration tool such as Airflow, Prefect, Dagster, or DVC
Ability to produce clear documentation including data dictionaries, dataset cards, and analysis reports
US Citizenship

Benefits

Comp & perks

Potential eligibility for overtime
Shift differential may be available
Discretionary bonus may be available