Our data scientists will leverage Python as a primary language to process data to accurately determine if resources and systems are secure; will look for outliers to help track the progress of systems through the Risk Management Framework lifecycle; will identify which System a Resource belongs to determined by various attributes of the identified lost Resource against known potential System information; will 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; will 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. Master's Degree in a quantitative discipline (i.e. statistics, mathematics, operations research, engineering or computer science) 2 years experience in software development, cloud development, analyzing datasets, or developing descriptive/predictive/prescriptive analytics can be substituted in lieu of Master's degree.