This role pays less than
80%
of similar roles. Most pay
$157,800–$204,250
— the shaded band above.
At the midpoint, this role pays about
$145k
versus about
$181k
for comparable roles.
Based on 240 similar postings.
Employer
About General Dynamics
General Dynamics is a global aerospace and defense company offering a broad portfolio of products and services in business aviation, ship construction, land combat vehicles, and information technology. It serves customers in the U.S. government, allied governments, and a diverse array of commercial markets.
General Dynamics currently has
1299 open roles
on FindRole.
Listed pay typically runs
$120,700–$149,500
across 1086 roles with salary data.
The Value Stream and AI Transformation Lead will guide the modernization of program execution processes while ensuring compliance with Earned Value Management System requirements and regulatory expectations. This transformation role involves mapping end-to-end value streams, identifying process constraints, and translating business needs into governed, AI-enabled workflows. The individual will lead value-stream mapping across planning, scheduling, cost management, and risk management to eliminate manual handoffs and automate processes. Key responsibilities include designing use cases for AI assistants, defining human oversight controls, and developing a prioritized transformation roadmap. The role requires expertise in Lean, systems thinking, business process architecture, and program controls. Candidates must possess a practical understanding of generative AI, workflow automation, and data governance to solve complex problems within highly regulated environments involving aerospace and defense contract requirements while ensuring auditability and cybersecurity standards are maintained throughout the lifecycle.
AI/ML
Automation
Data Management
Data Analysis
Data Visualization
Agile
Scrum
Kanban
Lean Six Sigma
PMI PMP
SAFe
Program Management
Process Improvement
Artificial Intelligence
Machine Learning
Natural Language Processing
Data Analytics
Software Development
Data Pipelines
Model Lifecycle Management
Structured Data
Unstructured Data
AI Platforms
Enterprise Data Ecosystems