AI/ML Decision Intelligence Lead

Responsibilities

Peraton Labs is seeking an AI/ML Decision Intelligence Lead to support the BNATCS CTO organization in developing advanced analytics, AI-enabled decision support, and agentic AI capabilities for one of the nation’s most significant aviation modernization efforts.

The BNATCS CTO is building the analytical, data, and AI foundation needed to help program and government stakeholders understand complex program dynamics, identify emerging risks, evaluate tradeoffs, and make better-informed decisions across a large-scale aviation system-of-systems environment. This includes integrating and analyzing programmatic, engineering, operational, and workstream data; developing decision-grade analytical products and shaping how advanced AI and analytics are applied to support execution, modernization, and mission outcomes.

This role will serve as a senior technical leader responsible for designing, developing, and guiding advanced analytics and AI capabilities that turn complex data into actionable insight. Candidates for this role should have deep hands-on technical depth, strong analytical judgment, comfort working across ambiguous data environments, and the ability to brief senior stakeholders with clarity, confidence, and credibility.

The ideal candidate for this position is a technical leader who can bridge artificial intelligence, machine learning, operations research, statistical modeling, systems thinking, and mission-focused execution by helping define analytical approaches, lead complex technical development efforts, guide data fusion strategies, mentor technical contributors, and shape the BNATCS CTO roadmap for AI-enabled decision intelligence and agentic analytics.

Key responsibilities may include, but are not limited to:

• Lead the design, development, and application of AI/ML, advanced analytics, and decision intelligence capabilities supporting BNATCS CTO priorities
• Transform complex programmatic, engineering, operational, and aviation-domain data into actionable insight for leadership, technical teams, and government stakeholders
• Develop analytical frameworks that support continuous monitoring, evaluation, and improvement of program performance, technical progress, operational readiness, and emerging risk areas
• Design and guide predictive models that identify early indicators of risk across schedule, cost, technical integration, operational performance, data readiness, and mission execution
• Apply machine learning, statistical modeling, operations research, simulation, optimization, and probabilistic methods to support scenario analysis, tradeoff evaluation, resource planning, and decision support
• Lead data fusion strategies across heterogenous data sources, including multi-source correlation, entity resolution, temporal alignment, data quality evaluation, lineage, and analytical readiness
• Identify, assess, and align data sources required to support advanced analytics, agentic AI workflows, visualization products, and decision-support capabilities
• Develop strategies for working with sensitive, incomplete, inconsistent, or restricted data, including potential use of synthetic data generation, privacy-preserving methods, and representative test datasets
• Evaluate opportunities to apply agentic AI, transformer-based methods, natural language processing, retrieval augmented generation, knowledge graphs, or other emerging AI techniques to mission relevant analytical workflows
• Ensure analytical methods are interpretable, reliable, testable, reproducible, and appropriate for high-governance, safety-conscious, and mission-critical environments
• Define evaluation approaches for AI/ML and analytical products, including performance measures, confidence communication, assumptions, limitations, validation methods, and operational suitability
• Partner with data, architecture, engineering, visualization, and workstream stakeholders to translate operational needs into measurable analytical outputs and technical delivery plans
• Create executive-ready briefings, analytical products, technical roadmaps, decision-support artifacts, and recommendations that communicate complex findings clearly to senior stakeholders
• Brief FAA, BNATCS, and internal leadership on analytical products, model results, decision implications, technical risks, capability gaps, and recommended paths forward
• Establish best practices for decision intelligence, model governance, reproducible analysis, documentation, data-driven program insight, and responsible AI use within the BNATCS environment
• Identify analytical risks early, including data quality concerns, weak assumptions, model drift, insufficient validation, governance gaps, or operational adoption barriers, and recommend mitigation strategies

Required Qualifications

• 12+ years of experience with a BS/BA, 10+ years with a MS/MA, or 6+ years with a PhD in AI/ML, data science, computer science, artificial intelligence, aerospace, systems engineering, or a related technical field
• Demonstrated experience developing or leading advanced analytics, AI/ML, predictive modeling, simulation, optimization, or decision-support capabilities in complex technical environments
• Strong ability to transform large, complex, or ambiguous datasets into decision-ready insights for senior technical, operational, programmatic, or government stakeholders
• Experience framing complex analytical problems, selecting appropriate methods, developing evaluation approaches, and communicating assumptions, limitations, confidence, and decision implications
• Experience with multi-source data integration, data fusion, data quality, assessment, temporal alignment, entity resolution, or analytical preparation of heterogenous datasets
• Experience applying machine learning or modern AI methods to real-world problems, with the judgment to balance model performance, interpretability, reliability, governance, and operational usefulness
• Experience supporting mission-critical, safety-conscious, highly regulated, government, aviation, defense, transportation, infrastructure, or large-scale systems environments
• Demonstrated ability to lead technical contributors, mentor analytical staff, review technical work, and guide multidisciplinary execution across AI/ML, analytics, data, and engineering teams
• Familiarity with agentic AI, intelligent workflow automation, human-machine teaming, decision intelligence, or AI-enabled operational support systems
• Experience with transformer-based models, natural language processing, retrieval augmented generation, knowledge graphs, GraphRAG, or semantic data integration
• Strong executive communication skills, including the ability to brief senior leaders on complex analytical, technical, and strategic topics
• Ability to work effectively with government stakeholders, technical teams, program leaders, and cross-functional partners in fast-moving and ambiguous environments
• US Citizenship with the ability to obtain/maintain an FAA Public Trust

Desired Qualifications

• Experience with FAA, aviation, air traffic, airline operations, transportation systems, National Airspace System modernization, or other complex system-of-systems domains
• Familiarity with MLOps, model monitoring, evaluation automation, reproducible pipelines, data lineage, model governance, and analytical lifestyle management
• Experience creating executive dashboards, analytical decision-support tools, operational insight products, or visualization strategies for senior decision makers
• Experience establishing analytical standards, governance practices, model evaluation frameworks, or responsible AI practices in high-governance environments

Target Salary Range$146,000 - $234,000. This represents the typical salary range for this position. Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual’s experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay.