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Data Scientist

Job in Fort Meade, Anne Arundel County, Maryland, USA
Listing for: Planet Works, Inc
Full Time position
Listed on 2026-05-23
Job specializations:
  • IT/Tech
    Data Scientist, Data Analyst, Machine Learning/ ML Engineer, Data Engineering
Salary/Wage Range or Industry Benchmark: 100000 - 125000 USD Yearly USD 100000.00 125000.00 YEAR
Job Description & How to Apply Below

Data Scientist

The ideal candidate for this position must possess the following:

  • Active TS clearance with polygraph.
  • AMOD tools experience.
  • A bachelor’s degree plus three (3) years of relevant experience.
  • A degree in Mathematics, Statistics, Machine Learning, Data Science, Computer Science is preferred.
  • Additional work experience or completion of JCAC (Joint Cyber Analysis Course) may be utilized to offset a lack of a degree.

The ideal candidate for this position should possess the following skills:

  • Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (at least one high level language, e.g. Python), statistical analysis (e.g., variability, sampling error, interference).
  • Proficient in Python and experienced in automating workflows, data manipulation, and visualization using Jupiter Notebooks.
  • Use analytic modeling, statistical analysis, programming, and/or another appropriate scientific method to assess large datasets in various states of organization, cleanliness, and structure that account for the unique feature and limitations inherent in National Agency data holding.
  • Translate practical mission needs and analytic questions related to large datasets into technical requirements and assist/collaborate with others to draw appropriate conclusions from the analysis of such data.
  • Be able to communicate with people of various backgrounds, including individuals with non-technical audiences.

Must be proficient in two or more of the following skill areas:

  • Foundations:
    Mathematical, Computational, Statistical
  • Data Processing:
    Data management and curation, data description and visualization, workflow and reproducibility
  • Modeling, Inference, and Prediction:
    Data modeling and assessment, domain-specific consideration.
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