Data Scientist

• 3-6 years’ experience in programming languages such as Python or R (statistical analysis), data preprocessing, and basic machine learning algorithms.\n
• Active TS/SCI with willingness to be approved for a Polygraph\n
• Bachelor’s Degree in related field (additional years of experience can be considered in lieu of degree)\n
• Working knowledge of ArcGIS, ArcSDE, ArcPro\n
• Familiarity with: common data science libraries and frameworks, plus

This interesting work requires staff qualified to have a basic understanding of GEOINT integration work. The work requires a basic understanding of GEOINT from collection, exploitation, analysis and product delivery capabilities.\n \n \nPrimary Responsibilities\n \n \n \n

• Provide data management and automation support to a specialized imagery and geospatial analysis division within a government or enterprise organization.\n
• Assist with developing a modernized data management workflow supporting multiple organizational programs.\n
• Collaborate with senior data scientists, database developers, and analysts to implement and sustain updated database processes and workflows.\n
• Research and document existing data management practices, procedures, and system dependencies.\n
• Help design improved workflows with automation incorporated from the outset.\n
• Ensure new workflows integrate effectively with modern database applications and enterprise-level application programming interfaces.\n
• Enhance existing models and simulations involving production, monitoring, and change-detection data.\n
• Support additional data management and modernization initiatives as required.\n
• Develop code and scripts, including the conversion of legacy applications written in multiple programming languages into Python-based solutions.\n
• Implement enterprise database management standards, processes, and controls.\n
• Automate repetitive data preparation, validation, transformation, and reporting tasks.\n
• Deliver technical solutions that apply industry best practices to modernize existing operational processes.\n
• Conduct data investigations using applied statistics, algorithm development, and collaboration with subject-matter experts.\n
• Translate complex analytical findings and operational realities into clear explanations for non-technical audiences.\n
• Create visualizations and dashboards from diverse data sources that are accessible and understandable to non-technical users.\n