The opportunity
SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today SpaceX is actively developing the technologies to make this possible, with the ultimate goal of enabling human life on Mars.
What you'll do
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.
What they're looking for
- 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.
