Senior Data Scientist

Responsibilities

• Develop capabilities to shift from the current manual system security evaluation and authorization process to a new model that emphasizes automation, streamlined processes and approvals, continuous monitoring and assessment, and network data gathering across the entire life cycle of a project.
• Leverage Python as a primary language to process data to accurately determine if resources and systems are secure.
• Look for outliers to help track the progress of systems through the Risk Management Framework lifecycle.
• Identify which System a Resource belongs to determined by various attributes of the identified lost Resource against known potential System information.
• Design, develop, and maintain ETL pipelines to extract security and compliance data from multiple sources (network sensors, security tools, compliance databases), transform the data for analysis and reporting, and load it into target data repositories to support continuous monitoring and automated assessments.
• Support data engineering operations including data quality validation, pipeline monitoring, and optimization of data workflows to ensure reliable, scalable, and timely delivery of security-related data for Risk Management Framework automation and decision‑making.

• TS/SCI with a Polygraph
• Bachelor’s and Master’s degree or higher from an accredited college or university in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science).
• Ten years of experience analyzing datasets and developing analytics.
• Ten years of experience programming with data analysis software such as R, Python, SAS, or MATLAB.
• An additional two years of experience in software development, cloud development, analyzing datasets, or developing descriptive, predictive, and prescriptive analytics can be substituted for a Master’s degree.
• A PhD from an accredited college or university in a quantitative discipline can be substituted for four years of experience.
• Background in statistical analysis.
• Experience with building, tuning, and testing predictive models.
• Experience creating analytic charts and dashboards.

• Python
• ETL pipelines
• Data analysis
• Statistical analysis
• Predictive modeling
• Data quality validation
• Pipeline monitoring
• Data transformation
• Data reporting
• Analytics

• TS/SCI with a Polygraph