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Shield AI, a venture-backed defense-tech company, is seeking a Staff Engineer, Industrial (Sustainment Analytics) to lead data-driven sustainment decisions for fielded aircraft systems. This role integrates maintenance, failure and repair, supply-chain, operational demand, configuration, labor, and fleet-trend data to identify drivers of downtime, maintenance burden, readiness risk, and lifecycle cost.
You will own sustainment decision analysis for fielded aircraft systems, including maintenance-level determinations, repair-versus-replace assessments, spares optimization, manpower analysis, and support-equipment trade studies. You'll build and maintain integrated fleet-readiness and availability models that link reliability, maintainability, maintenance demand, repair turnaround time, inventory and supply lead times, labor availability, operational utilization, repair capacity, and cost to readiness outcomes.
Key responsibilities include developing repair-versus-replace and repair-network business cases; analyzing maintenance records, work orders, failure and repair history, and supply/logistics data to identify recurring burdens and high-value opportunities; developing demand forecasts and spares recommendations; performing manpower, workload, and capacity analyses; evaluating investments in support equipment and repair capability; developing lifecycle-cost models and economic trade studies; applying Pareto, trend, statistical, sensitivity, and scenario analyses; translating complex analyses into clear recommendations and executive briefings; and establishing repeatable sustainment analytics processes with data standards, modeling methods, and lessons-learned feedback loops.
You will partner closely with Fleet Support Engineering, Reliability and Maintainability Engineering, Product Engineering, Reliability Data Engineering, Supply Chain, Manufacturing, Finance, and Operations to ensure sustainment decisions improve fleet availability, customer support, affordability, and long-term system performance.
REQUIREMENTS:
- Bachelor's degree in Industrial Engineering, Systems Engineering, Operations Research, Data Analytics, or related technical discipline; equivalent practical experience considered.
- 5+ years of experience in industrial engineering, sustainment, logistics, supportability, maintenance, operations research, fleet operations, aviation sustainment, defense logistics, or complex hardware-support environments.
- Demonstrated experience using maintenance, reliability, repair, inventory, supply-chain, production, fleet-operations, or fielded-hardware data to develop quantitative analyses and recommendations related to availability, readiness, repair capability, logistics, labor, capacity, cost, and operational performance.
- Experience performing repair-versus-replace, make-versus-buy, repair-capability, or comparable sustainment trade studies; ability to build structured models, quantify assumptions, conduct sensitivity analyses, compare alternatives, and communicate uncertainty and risk.
- Proficiency with SQL, Python, Power BI, Tableau, Excel, R, MATLAB, or comparable tools for data analysis, modeling, visualization, and reporting.
- Ability to integrate technical, operational, supply-chain, labor, and financial inputs into practical, data-driven recommendations for complex sustainment decisions while balancing analytical rigor, data limitations, operational urgency, customer impact, fleet readiness, cost, and long-term sustainment needs.
- Strong cross-functional collaboration and communication skills; ability to work with engineering, fleet support, supply chain, manufacturing, finance, operations, and analytics teams; develop clear decision packages, technical analyses, recommendations, and leadership-level summaries; independently drive decisions without direct authority.
PREFERRED:
- Familiarity with aviation, defense, aerospace manufacturing, unmanned systems, aircraft sustainment, and deployed hardware systems.
- Familiarity with RCCA, FRACAS, PQDR, AS9100, service bulletins, maintenance releases, supply chain and workforce management.
- Proficiency with SQL, Excel, Python, Salesforce, Foundry, or similar platforms.
- Demonstrated ability to collect, clean, join, and structure data from multiple operational, maintenance, failure, or quality systems.
- Experience developing metrics, dashboards, or recurring reports that improved decision-making, increased visibility, or reduced manual reporting effort.
- Demonstrated success identifying and correcting data-quality, traceability, or reporting issues before they affected technical or business decisions.
- Ability to translate complex data into clear, decision-ready information for technical and non-technical audiences.
- Experience supporting military, government, international, or deployed aviation customers.
- Familiarity with ITAR, export-controlled technical data, or controlled customer environments.
- Active Secret or Top Secret clearance.
