AI​/ML Decision Intelligence Lead

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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.

• 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…