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Scienceabode > Data Scientist – TS/SCI with Poly

Data Scientist – TS/SCI with Poly

Last updated: 2025/09/16 at 8:25 AM
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  • Permanent
  • United States
  • Posted 2 days ago
Macpower Digital Assets Edge Private Limited

Website Macpower Digital Assets Edge Private Limited

Macpower Digital Assets Edge Private Limited

Company : Macpower Digital Assets Edge Private Limited

Job Summary: This position is with a Data Science consulting firm specialized in providing analytic solutions to clients in Commercial and Government industries. Providing analytic solutions to hundreds of companies across numerous industries, our team enjoys a great variety in the type of work they do and exposure to a wide range of techniques and tools. To fill this role, you must have Active TS/SCI security clearance with the full scope Polygraph that is required.

Position Responsibilities: A data scientist will develop machine learning, data mining, statistical and graph-based algorithms to analyze and make sense of datasets; prototype or consider several algorithms and decide upon final model based on suitable performance metrics; build models or develop experiments to generate data when training or example datasets are unavailable; generate reports and visualizations that summarize datasets and provide data-driven insights to customers; partner with subject matter experts to translate manual data analysis into automated analytics; implement prototype algorithms within production frameworks for integration into analyst workflows.

Required Degree and Experience: Bachelor's degree in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science) + 7 years of experience, Master's Degree + 5 years of experience, or PhD + 2 years of experience

Required Skills:

  • Programming experience with data analysis software such as R, Python, SAS, or MATLAB.
  • Develop experiments to collect data or models to simulate data when required data are unavailable.
  • Develop feature vectors for input into machine learning algorithms.
  • Identify the most appropriate algorithm for a given dataset and tune input and model parameters.
  • Evaluate and validate the performance of analytics using standard techniques and metrics (e.g. cross validation, ROC curves, confusion matrices).
  • Oversee the development of individual analytic efforts and guide team in analytic development process.
  • Guide analytic development toward solutions that can scale to large datasets.
  • Partner with software engineers and cloud developers to develop production analytics.
  • Develop and train machine learning systems based on statistical analysis of data characteristics to support mission automation.

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