Operations Analyst, Reliability (Starship)

Responsibilities

• Identify opportunities in reliability processes (e.g., predictive modeling, anomaly detection); define problems, develop prompts/workflows using LLMs or generative AI, evaluate outputs, monitor performance, provide go-live support, and create operational protocols—partnering with software engineering to define implementation direction while maintaining ownership of outcomes.
• Design, implement, and maintain dashboards (e.g., Grafana, PowerBI, Tableau, SQL reports) for monitoring build metrics (defect rates, tolerances, nonconformances) and flight indicators.
• Maintain and optimize dashboards and tools for ongoing accuracy and usability, including troubleshooting issues, updating data feeds, and ensuring seamless integration with user workflows across production, quality, and reliability teams.
• Improve data infrastructure (pipelines, ETL, schemas) for scalability, integrity, and real-time use, with ownership of data integrity to support reliable AI tools and initiatives.
• Develop/track KPIs for build health and flight risk, including yields, pass rates, traceability, and predictive models to inform decisions and actions.
• Analyze datasets (SQL, Python) to find root causes of anomalies, correlate build/flight data, and recommend solutions; use AI for pattern recognition and forecasting.
• Collaborate with production, design, reliability, and software teams to define data requirements for emerging use cases, such as integrating build data with flight test results, and develop automation scripts to mitigate risks, streamline reporting, and enhance efficiencies.
• Create ad-hoc queries, reports, and simulations for workflows, nonconformances, constraints, and gaps.
• Standardize data capture for manufacturing processes (e.g., welding, assembly, NDE, and checkouts), ensuring comprehensive traceability and feedback loops that capture lessons learned from builds and flights to prevent repeat issues.
• Train users/leaders on tools, data interpretation, AI workflows, and best practices; share knowledge to build expertise in analytics and AI.
• Actively contribute creative thinking and leadership to solving complex problems under tight deadlines, with a focus on "reliability by any means necessary" to align production scaling with mission success.

Qualifications

• Bachelor's degree.
• 1+ years in building and operating metric based dashboards.
• 1+ years of experience building data structures and query optimizations.

Prior Experience

• Experience in a manufacturing or engineering environment.
• Experience working in a factory or production environment.
• 3+ years of experience with SQL, Python, C++, Tableau, or PowerBI.