Staff Engineer, Industrial (R6313)

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube. Job Description: The Staff Industrial Engineer, Sustainment Analytics, leads data-driven sustainment decisions by integrating maintenance, FRACAS, 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. The role translates these insights into structured analyses, defensible trade studies, and actionable recommendations that optimize maintenance levels, repair capability, spares, manpower, support equipment, processes, and sustainment strategy—improving fleet readiness, repair throughput, maintenance effectiveness, and total cost of ownership. The engineer partners 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. What you'll do: Own sustainment decision analysis for fielded aircraft systems, including maintenance-level determinations; repair-versus-replace, repair-location, and repair-capability assessments; spares optimization; manpower analysis; and support-equipment trade studies. 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. Develop repair-versus-replace and repair-network business cases that assess discard, organizational/field-level repair, depot or centralized repair, supplier return, and redesign alternatives using repair yield, turnaround time, labor, test-equipment requirements, supply lead times, fleet impact, risk, and lifecycle cost. Analyze maintenance records, work orders, failure and repair history, supply and logistics data, labor data, and fleet operations data to identify recurring maintenance burdens, demand drivers, capacity constraints, bottlenecks, readiness risks, and high-value sustainment opportunities. Develop demand forecasts, spares recommendations, and provisioning strategies using failure rates, consumption history, repair turnaround times, operational tempo, lead times, service-level targets, and deployment requirements. Perform manpower, workload, capacity, and skill-mix analyses to define staffing needs, identify workload constraints and maintenance bottlenecks, and improve repair throughput and maintenance effectiveness. Evaluate investments in support equipment, test equipment, tooling, facilities, and repair capability by quantifying capacity, utilization, cost, risk reduction, readiness impact, return on investment, and total lifecycle value. Develop lifecycle-cost models and economic trade studies to inform sustainment planning, maintenance-program changes, provisioning decisions, repair-network strategy, and leadership investment decisions. Apply Pareto, trend, statistical, sensitivity, and scenario analyses to prioritize actions that improve fleet availability, reduce downtime, lower sustainment cost, and mitigate readiness risk. Translate complex analyses into clear recommendations, decision packages, executive-ready briefings, and prioritized action plans for sustainment leadership and cross-functional stakeholders. Establish repeatable sus…

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