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AI​/ML Decision Intelligence Lead

Job in Corridor North, Howard County, Maryland, USA
Listing for: Peraton
Full Time position
Listed on 2026-07-01
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), AI Business & Operations, AI Evaluation, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 146000 - 234000 USD Yearly USD 146000.00 234000.00 YEAR
Job Description & How to Apply Below
Location: Corridor North

AI/ML Decision Intelligence Lead Job Location s

US-VA-Herndon | US-MD-Bowie | US-MD-Annapolis Junction

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.

This ideal candidate will bridge artificial intelligence, machine learning, operations research, statistical modeling, systems thinking, and mission‑focused execution by defining analytical approaches, leading complex technical development, guiding data fusion strategies, mentoring contributors, and shaping the BNATCS CTO roadmap.

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 heterogeneous 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,…
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