Siemens Mobility is looking to solve the problem of unexpected locomotive engine failures by developing a predictive maintenance strategy. This involves transforming raw sensor data into actionable insights to prevent breakdowns, reduce maintenance costs, and ensure reliable transportation for passengers and freight.
Requirements
- Majoring in Engineering (Mechanical, Electrical, Computer, Industrial), with a strong technical or analytical aptitude
- Prior internship or co-op experience (preferred)
- Demonstrated leadership potential
- Strong analytical, writing, presentation, and critical-thinking skills
- Industry knowledge and/or interest in data analytics and mechanical systems
Responsibilities
- Analyze engine performance data using advanced analytics tools to identify trends, anomalies, and potential failure modes
- Develop and refine algorithms that can predict engine maintenance needs weeks or months in advance
- Work alongside engineers, data scientists, and field technicians to validate findings and implement monitoring improvements
- Contribute to next-generation monitoring technologies, including IoT sensors, machine learning applications, and digital twin development
- Translate technical findings into actionable recommendations that directly improve locomotive reliability and reduce downtime
- Receive mentoring from seasoned professionals across multiple fields to help guide your future
Other
- 10-12-week internship
- Current Sophomore or Junior undergraduates
- 40 hours per work from June to August
- Applicants must be legally authorized for employment in the United States without the need for current or future employer-sponsored work authorization
- Finding creative solutions when data tells an incomplete story
- Connecting the dots between complex technical symptoms and root causes
- Working effectively with diverse teams from software developers to field mechanics
- Taking ownership of analysis projects and driving them to completion
- Asking "why" and "what if" to uncover hidden insights in data
