Lead Data Scientist

Data Science LeaderAir is the leader in Enterprise Readiness. Our mission is to establish readiness as a real-time condition that is continuously achieved. Today, a dangerous Readiness Gap exists between what the front line needs and what is delivered. Our AI-native platform, Air Enterprise Readiness, aligns development, production, delivery, and sustainment into one coordinated execution system for government agencies and industrial suppliers. By revealing true capacity, exposing real constraints, coordinating resources, and executing at the speed of operational demands, the front line gets what it needs to succeed. We are seeking an exceptional Data Science leader to technically lead and own major areas of Air's core AI and Data Science, advancing the modeling, algorithms, and scientific foundations that power our products, including the data science behind Enterprise Readiness. This leader will set the strategic direction for Core Data Science and Applied AI, identify and prioritize high-impact technical opportunities, and drive the development of reusable modeling capabilities. The role will own the end-to-end Data Science lifecycle, from problem formulation and model development and training through evaluation and validation in real-world, end-to-end use cases. We are seeking a leader who can combine deep technical expertise with strong organizational leadership to build a high-impact core science function and translate advanced Data Science into capabilities that scale across the company. This role is a full-time position located out of our office in Arlington, VA / Pittsburgh, PA / San Francisco, CA. This role may require up to 10% travel. Scope of Responsibilities Lead Core Data Science Strategy: Define and drive the technical roadmap for core Data Science capabilities, translating complex client and business challenges into scalable data science and Applied AI across the Air Enterprise readiness. Set Scientific Standards and Drive Innovation: Establish best practices for model architecture, experimentation, evaluation, and deployment; assess emerging research and technologies; and translate scientific advances into robust, cost-effective, production-ready capabilities. Advance Core Modeling Capabilities: Drive the development of state-of-the-art methods across specialized areas such as simulation, optimization, forecasting, predictive modeling, reinforcement learning, and foundation models, applying deep expertise in one or more domains. Own End-to-End Scientific Execution: Lead large-scale Data Science projects from problem formulation and ideation through model development, training, evaluation, and production deployment, ensuring solutions deliver measurable client impact. Build Scalable Decision Intelligence Platforms: Working together with the MLOps team, guide the development of reusable modeling capabilities that integrate with data platforms, Agentic AI systems, and client workflows to power complex decision intelligence solutions across diverse defense use cases. Drive Cross-Functional Technical Leadership: Align Data Science, Applied AI, Forward Deployed Data Science, Product, Platform, and Engineering teams on scientific priorities, technical architecture, project execution, and evaluation criteria tied to client and product objectives. Develop Talent and Organizational Excellence: Mentor and develop Data Scientists across experience levels, foster deep expertise in core scientific disciplines, and cultivate a culture of technical excellence, rigorous experimentation, knowledge sharing, and continuous learning.