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Data Scientist

Peraton

$135,000 - $216,000
Sep 9, 2025
Linthicum, MD, US
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Peraton is seeking a Data Scientist to analyze data sources, conduct hypothesis testing, program custom algorithms, build predictive models, and perform complex analytical research to inform operational requirements and solve daunting challenges for national security.

Requirements

  • Knowledge of programming language structures and logic.
  • Skill in conducting queries and developing algorithms to analyze data structures.
  • Skill in creating and utilizing mathematical or statistical models.
  • Ability to interpret and incorporate data from multiple tool sources.
  • Ability to use and understand complex mathematical concepts (e.g., discrete math).
  • Skill in developing or recommending analytic approaches or solutions to problems and situations for which information is incomplete or for which no precedent exists.
  • Ability to build complex data structures and high-level programming languages.

Responsibilities

  • Analyze data sources to provide actionable recommendations.
  • Conduct hypothesis testing using statistical processes.
  • Program custom algorithms.
  • Utilize technical documentation or resources to implement a new mathematical, data science, or computer science method.
  • Build predictive, prescriptive, or descriptive models in collaboration with stakeholders.
  • Plan and conduct complex analytical, mathematical, and statistical research that informs operational requirements.
  • Plan, coordinate, and execute complex studies using advanced data modeling techniques and procedures, data trend analysis, and data algorithms.

Other

  • Associates degree and 10+ years of experience; OR Bachelor's degree and 8+ years of experience; OR Master's Degree and 6+ years of experience; OR 3 years with PhD.
  • Active Secret clearance.
  • Desired Certifications: CCISO, CISM, CISSP, or GSLC.
  • Ability to collect, verify, and validate test data.
  • Ability to dissect a problem and examine the interrelationships between data that may appear unrelated.