Senior Data Scientist - Operational Analytics

Peraton Labs is seeking a Data Scientist to support the BNATCS CTO Advanced Analytics and Digital Command Center teams by transforming complex workstream, program, schedule, requirements, and operational data into trusted metrics, analytical insights, and decision-support products.

This role will support the analytics foundation behind the Digital Command Center (DCC) by helping interpret, model, visualize, and operationalize complex workstream data. You will work closely with CTO leadership and mission stakeholders to transform raw and evolving program data into actionable insights, dashboards, metrics, and analytics products that support FAA modernization objectives.

The ideal candidate for this role brings strong analytical judgment, cognitive flexibility, hands-on comfort with data, and the ability to understand both the technical structure of data and the operational meaning behind it. Candidates for this role should be able to analyze, reason, and communicate well enough to contribute to analytics workflows, dashboard logic, data validation, and integration activities supporting the consolidated data environment that feeds the program’s agentic AI engine.

Experience with FAA, NAS, Aviation, air traffic operations, transportation, safety-critical systems, or large-scale modernization data is highly relevant to this position.

Key responsibilities may include, but are not limited to:

• Analyze workstream, requirements, IMS, schedule, risk, operational, and program data to identify trends, dependencies, gaps, anomalies, and decision-relevant insights
• Define operational metrics, KPIs, analytical logic, thresholds, and decision-support views for Digital Command Center dashboards and advanced analytics products
• Assess the quality, completeness, consistency, and usability of data made available through enterprise data repositories
• Partner with data architects and data engineers to identify data gaps, transformation needs, metadata requirements, and analytics-readiness issues
• Support ai-enabled analytics workflows by helping define trusted data inputs, context, validation criteria, and human-in-the-loop review considerations
• Develop notebooks, scripts, SQL queries, statistical analyses, models, and reusable analytical artifacts to support data exploration and decision-making
• Translate ambiguous operational questions into structured analysis, measurable metrics, and clear recommendations
• Communicate assumptions, limitations, findings, and implications to technical teams, program leadership, and mission stakeholders

Required Qualifications

• 8+ years of experience with a BS/BA, 6+ years with a MS/MA, or 3+ with a PhD in Data Science, Computer Science, Statistics, Mathematics, Engineering, Operations Research, Information Systems, Analytics, or a related technical discipline
• Experience analyzing complex structured, semi-structured, operational, program, or business datasets to support decision-making
• Proficiency with Python, R, SQL, or similar analytical tools, with the ability to develop notebooks, scripts, queries, and reusable analysis artifacts
• Experience with exploratory data analysis, data cleaning, statistical analysis, trend analysis, anomaly detection, forecasting, classification, clustering, or predictive modeling
• Experience defining or supporting metrics, KPIs, dashboards, reports, or decision-support products
• Ability to assess data quality, completeness, consistency, and analytical usefulness
• Ability to translate ambiguous operational and stakeholder questions into structured analyses and measurable outputs
• US Citizenship