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Peraton

Runs and secures technology for federal agencies

Data Scientist

Data Science +1Posted on Nov 5
Full-timeRed Bank, NJ, USAIn-person

About the Job

Requirements

  • A bachelor's degree or higher in Computer Science, Statistics, Mathematics, Electrical Engineering, or a related technical field is required.
  • At least 5 years of applied data science or data engineering experience is required, or a master's degree with at least 3 years of experience, with a record of delivering data pipelines and analyses used by engineers and researchers.
  • Strong Python proficiency, including NumPy, pandas, SciPy, and scikit-learn, and experience with at least one deep learning framework are required; PyTorch is preferred.
  • Hands-on experience with knowledge graphs, ontologies, or structured knowledge representation such as RDF/OWL or property graphs such as Neo4j, and experience querying and validating them with SPARQL, Cypher, SHACL, or similar tools, is required.
  • Experience designing data schemas, metadata standards, and annotation workflows for large scientific or engineering datasets, including data versioning and provenance, is required.
  • A solid foundation in experimental design, hypothesis testing, and uncertainty quantification, and ability to explain results to technical and non-technical audiences, is required.
  • Experience with SQL and at least one workflow or pipeline orchestration tool such as Airflow, Prefect, Dagster, or DVC is required.
  • Ability to produce clear documentation, including data dictionaries, dataset cards, and analysis reports, is required.
  • U.S. citizenship is required.

Responsibilities

  • Design, build, and populate research-team knowledge bases, and maintain them under version control with provenance tracking.
  • Develop rule-based, statistical, and machine-learning-assisted knowledge extraction 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 program datasets so they 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 showing 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 and monthly status reports, prepare dataset documentation, and coordinate with academic subcontractors on shared data and knowledge resources.

Desired Qualifications

  • Experience with formal methods or formal verification, such as SMT solvers including Z3 or cvc5, model checkers, property-based testing such as Hypothesis, or proof assistants, particularly for validating generated programs or signal-processing pipelines.
  • Experience designing or implementing domain-specific languages, including grammar design, parser generators such as ANTLR or Lark, type systems, intermediate representations, or compiling DSL programs to executable code.
  • Background in symbolic AI or neuro-symbolic methods, including logic programming, constraint solving, rule engines, or program synthesis.
  • Familiarity with digital signal processing and communications fundamentals, including modulation, filtering, coding, and channel effects, or with GNU Radio and software-defined radio data formats such as I/Q sample handling and SigMF metadata standards.
  • Prior work on IARPA, DARPA, or similar government research programs, including data-sharing plans, privacy protection plans, and delivery of datasets to independent testing and evaluation teams.
  • Experience building causal or probabilistic models from structured knowledge, or working alongside causal inference researchers.
  • A master's or PhD in Computer Science, Statistics, Electrical Engineering, or a related technical field.
  • Willingness and ability to obtain a Secret security clearance.

About the Company

Peraton

Runs and secures technology for federal agencies

Peraton builds and runs secure technology for American defense, intelligence, space, and civilian agencies, from military networks to satellite ground stations. In 2025, the Federal Aviation Administration chose it to manage the overhaul of the nation's air traffic control system, replacing copper wiring with fiber and upgrading radar and radios. Its engineers, many holding security clearances, run and defend agency networks and data centers, write mission software, and operate space and communications systems, and agencies pay for this work through multiyear contracts. Peraton was formed in 2017, when private-equity firm Veritas Capital bought Harris Corporation's government IT business, and is based in Reston, Virginia.

Company size
10,001+
Company stage
Grant
Total funding
$60M
Headquarters
Reston, VA
Founded
2017

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