AI/ML Modeling Team Lead

About the Role

Redhorse transforms the way government uses data and technology. To support this mission, we are seeking an AI/ML Modeling Lead to support the United States Air Force (USAF) Rapid Sustainment Office (RSO) Condition Based Maintenance Plus (CBM+) program.

In this role, you will lead the modeling team in translating strategic vision into practical, data-driven solutions that treat data as a product. By fusing historical aircraft maintenance records with real-time sensor telematics, you and your team will deploy predictive models that forecast component failures, optimize fleet maintenance schedules, increase aircraft availability, and minimize operational downtime. Operating within the USAF’s Advana environment, you will ensure our AI/ML solutions are scalable, governed, explainable, and directly aligned with warfighter readiness.

Key Responsibilities

• Team Leadership & Execution: Lead day-to-day modeling team delivery, driving task prioritization, sprint planning, peer reviews, and cross-functional coordination.
• Requirements Translation: Translate complex mission objectives, operational challenges, and stakeholder inputs into defined modeling requirements, assumptions, and acceptance criteria.
• Technical Architecture & Guidance: Provide architectural direction on modeling techniques, feature engineering, data inputs, validation protocols, and performance tradeoffs to ensure solutions are reliable, explainable, and fit-for-purpose.
• Quality & Governance: Review modeling pipelines, system documentation, and analytical products for quality, consistency, and compliance with enterprise data governance standards.
• Cross-Functional Collaboration: Partner closely with data engineers, software developers, product owners, and USAF stakeholders to integrate models into scalable production pipelines within enterprise platforms.
• Risk & Blocker Mitigation: Proactively identify technical dependencies, resource bottlenecks, and delivery risks, providing actionable mitigation paths to program leadership.
• Mentorship & Best Practices: Mentor data scientists and analysts by sharing engineering best practices, establishing repeatable modeling frameworks, and championing disciplined version control and documentation habits.

Required Experience/Clearance

• Security Clearance: Active DoD Secret clearance. Applicants must currently possess an active Secret clearance to be considered. Candidates without an active clearance will not be considered.
• Professional Experience: 6+ years of progressive experience in data science, advanced analytics, or machine learning, including experience leading technical projects or multidisciplinary teams.
• Data Strategy & Execution: Demonstrated track record of leveraging data products to drive operational strategies and high-impact business outcomes.
• Agile Leadership: Proven experience managing technical workflows within collaborative, agile development environments (e.g., Scrum, Kanban).
• Stakeholder Communication: Exceptional ability to articulate complex AI/ML methodologies, validation results, and technical concepts to non-technical operational leaders.
• Platform & Tool Familiarity: Solid working knowledge of modern data platforms, cloud architectures, and analytics visualization tools (e.g., Power BI, Tableau).
• Education: Master’s degree in a quantitative discipline (Data Science, Computer Science, Engineering, Mathematics, Business Analytics) or equivalent practical experience.

Preferred Skills/Education

We encourage all candidates who meet the basic requirements to apply, even if you do not have any of the following experience:

• Advana / WDP / BLADE: Direct experience working within the CDAO War Data Platform (WDP / Advana) or the USAF BLADE analytics enclave.
• Defense Logistics & CBM+: Domain knowledge of Department of Defense maintenance operations, supply chain logistics, aviation telemetry, or Condition Based Maintenance Plus (CBM+).
• End-to-End Lifecycle Modeling: Hands-on experience executing full-lifecycle modeling workflows, including data cleaning, model design, validation, calibration, hyperparameter tuning, and production documentation.
• Data Quality Frameworks: In-depth understanding of enterprise data governance, data lineage, and automated data quality validation.
• Industry Certifications: Relevant technical or management credentials such as Project Management Professional (PMP), Certified Scrum Product Owner (CSPO), or cloud/data architecture certifications.