AI/ML Decision Intelligence Lead

Peraton•Bowie, MD

2d•$146,000 - $234,000

About The Position

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 a major aviation modernization effort. The BNATCS CTO is building analytical, data, and AI foundations to help program and government stakeholders understand complex program dynamics, identify emerging risks, evaluate tradeoffs, and make informed decisions across a large aviation system-of-systems environment. The work includes integrating and analyzing programmatic, engineering, operational, and workstream data; creating decision-grade analytical products; and shaping the use of advanced AI and analytics to support execution, modernization, and mission outcomes. This senior technical leadership role designs, develops, and guides advanced analytics and AI capabilities that turn complex data into actionable insight. It calls for deep hands-on technical expertise, sound analytical judgment, comfort with ambiguous data environments, and the ability to communicate clearly and credibly with senior stakeholders. The role bridges AI, machine learning, operations research, statistical modeling, systems thinking, and mission-focused execution by defining analytical approaches, leading complex development efforts, guiding data fusion strategies, mentoring technical contributors, and shaping the BNATCS CTO roadmap for AI-enabled decision intelligence and agentic analytics.

Requirements

12+ years of experience with a BS/BA, 10+ years with an 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.
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 heterogeneous datasets.
Experience applying machine learning or modern AI methods to real-world problems, balancing 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.
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 and ability to obtain or maintain an FAA Public Trust.

Nice To Haves

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 lifecycle 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.

Responsibilities

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 for continuous monitoring, evaluation, and improvement of program performance, technical progress, operational readiness, and emerging risk areas.
Design and guide predictive models to 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 scenario analysis, tradeoff evaluation, resource planning, and decision support.
Lead data fusion strategies across heterogeneous data sources, including multi-source correlation, entity resolution, temporal alignment, data quality evaluation, lineage, and analytical readiness.
Identify, assess, and align data sources for advanced analytics, agentic AI workflows, visualization products, and decision-support capabilities.
Develop strategies for working with sensitive, incomplete, inconsistent, or restricted data, potentially using 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 suitable 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 clearly communicate complex findings 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, and operational adoption barriers—and recommend mitigation strategies.