NOBLIS IS SEEKING A CLEARED DATA SCIENTIST SME (MULTIPLE LEVELS) WITH IC EXPERIENCE AND ACTIVE TOP SECRET WITH SCI AND POLYGRAPH IN MCLEAN, VA AND BETHESDA, MD

The Data Scientist SME will be responsible for and not limited to:

• Driving data science effectiveness by evaluating analytical capabilities, conducting technique assessments, identifying methodology gaps, establishing performance metrics, and aligning with organizational business objectives.
• Leading continuous improvement initiatives through data process analysis, architectural enhancements, analytical workflow optimization, and capability enhancement roadmaps.
• Enhancing predictive modeling capabilities by designing implementation procedures, developing standardized approaches for different model types, establishing validation protocols, and coordinating cross-functional model deployment activities.
• Improving business intelligence through effective reporting templates, actionable data dashboards, real-time visualization mechanisms, and contextual insight distribution.
• Optimizing analytics tool usage by maximizing platform effectiveness, fine-tuning algorithms, synthesizing multi-source data, ensuring system integration, and recommending technical enhancements.
• Implementing data automation by identifying suitable processes, designing ETL workflows, developing code scripts, implementing automated analysis responses, and documenting procedures.
• Managing the data reporting lifecycle from collection to distribution, ensuring timely delivery, implementing feedback mechanisms, establishing archival procedures, and meeting compliance requirements.
• Fostering agile data science approaches through methodologies like analytics sprints, retrospectives, iterative development, and balancing innovation with governance requirements.
• Coordinating cross-functional data initiatives by serving as a liaison between teams, aligning analytics with organizational goals, facilitating knowledge sharing, and building stakeholder relationships.
• Leading data integration through architecture design, API implementation, data normalization, integration testing, and comprehensive documentation.
• Driving advanced analytics by developing collection strategies, implementing statistical techniques, creating insightful visualizations, designing KPIs, and leveraging data for strategic decision-making.

Required Qualifications:

• Experience possessing in-depth knowledge of: Data science toolkits, data analytics and statistical analysis as well as data discovery, use and exploitation within multi-cloud environments.
• Experience with in-depth knowledge of standard data science languages (SQL, R, Python, Julia).
• Experience understanding Data integration and data management fundamentals and how to successfully adopt those for data science projects from development into production.
• Experience with understanding customer needs and visualizing data in the most effective way possible for a given project or study.
• Experience related to working with unstructured datasets.
• Experience with technical writing, ability to communicate complex data in simple, actionable ways.

REQUIRED MINIMUM SKILLS AND KNOWLEDGE:

• US Citizenship is required.
• Active Top Secret/SCI clearance with Polygraph.
• Bachelor’s degree in computer science, data science, or a related field.

• Driving data science effectiveness by evaluating analytical capabilities, conducting technique assessments, identifying methodology gaps, establishing performance metrics, and aligning with organizational business objectives.
• Leading continuous improvement initiatives through data process analysis, architectural enhancements, analytical workflow optimization, and capability enhancement roadmaps.
• Enhancing predictive modeling capabilities by designing implementation procedures, developing standardized approaches for different model types, establishing validation protocols, and coordinating cross-functional model deployment activities.
• Improving business intelligence through effective reporting templates, actionable data dashboards, real-time visualization mechanisms, and contextual insight distribution.
• Optimizing analytics tool usage by maximizing platform effectiveness, fine-tuning algorithms, synthesizing multi-source data, ensuring system integration, and recommending technical enhancements.
• Implementing data automation by identifying suitable processes, designing ETL workflows, developing code scripts, implementing automated analysis responses, and documenting procedures.
• Managing the data reporting lifecycle from collection to distribution, ensuring timely delivery, implementing feedback mechanisms, establishing archival procedures, and meeting compliance requirements.
• Fostering agile data science approaches through methodologies like analytics sprints, retrospectives, iterative development, and balancing innovation with governance requirements.
• Coordinating cross-functional data initiatives by serving as a liaison between teams, aligning analytics with organizational goals, facilitating knowledge sharing, and building stakeholder relationships.
• Leading data integration through architecture design, API implementation, data normalization, integration testing, and comprehensive documentation.
• Driving advanced analytics by developing collection strategies, implementing statistical techniques, creating insightful visualizations, designing KPIs, and leveraging data for strategic decision-making