Staff Engineer, Industrial (R6313)

Job Description

Job Description

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 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 sustainment analytics processes, including data standards, modeling methods, decision criteria, assumptions management, and lessons-learned feedback loops; mature the capability toward predictive maintenance, condition-based maintenance, and proactive fleet-health decision support.

Required qualifications:

• Bachelor’s degree in Industrial Engineering, Systems Engineering, Operations Research, Data Analytics, or a 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 other 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; able 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, including the 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; and independently drive decisions without direct authority.

Preferred qualifications:

• 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 data analysis and visualization tools such as 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.

#LD

Full-time regular employee offer package:

Pay within range listed + Bonus + Benefits + Equity

Temporary employee offer package:

Pay within range listed above + temporary benefits package (applicable after 60 days of employment)

Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.

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Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.