Data Scientist Engineer Level 2

Key Responsibilities

• Develop and operationalize advanced analytics: Design, develop, test, and deploy machine learning, statistical, data mining, and graph-based algorithms to solve complex, mission-critical challenges.
• Conduct applied research and experimentation: Evaluate emerging technologies, modeling approaches, and analytical methodologies; design experiments and select solutions based on performance, scalability, data availability, and mission requirements.
• Transform data into actionable insights: Analyze complex datasets, develop models and projections, and create visualizations and technical products that communicate meaningful, data-driven insights to customers and stakeholders.
• Automate and integrate analytics: Partner with subject matter experts and analysts to translate manual processes into automated analytics and integrate prototype algorithms into production systems and operational workflows.
• Drive technical collaboration and customer engagement: Serve as a technical representative in customer meetings and reviews, collaborate across engineering and research teams, and communicate technical findings and recommendations to diverse audiences.
• Advance mission capabilities: Maintain awareness of emerging AI/ML and data science technologies, identify opportunities for innovation, and contribute to the development of scalable solutions that deliver measurable mission impact.
• #MDFSP
• Bachelor's degree from an accredited college or university in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering, or computer science) and five (5) years of experience analyzing datasets and developing analytics, as well as five (5) years of experience programming with data analysis software such as R, Python, SAS, or MATLAB. An additional four (4) years of experience in software development, cloud development, analyzing datasets, or developing descriptive, predictive, and prescriptive analytics may be substituted for a bachelor's degree. A Ph.D . from an accredited college or university in a quantitative discipline may be substituted for four (4) years of experience.
• Experience with produce data visualizations that provide insight into dataset structure and meaning
• Work with subject matters experts (SMEs) to identify important information in raw data and develop scripts that extract this information from a variety of data formats (e.g., SQL tables, structured metadata, network logs)