Mobile Fleet Telemetry Specialist
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Key skills for this role
About the Role
The role involves expertise in mobile fleet telemetry for predictive maintenance, collaborating with AI teams, and analyzing multi-sensor data for mining operations.
Key Skills for This Role
Responsibilities
- Serve as the mobile fleet telemetry expert for predictive maintenance and failure identification
- Translate haul truck and shovel telemetry into signals, failure modes, and features for AI models
- Interpret multi sensor and telemetry data to explain healthy and failing conditions
- Ground truth and label failure events for model training
- Validate model outputs against field reality and flag false alarms
- Bring failure mode knowledge of engines, drivetrains, hydraulics, and structures into model design
- Partner with site teams to confirm predictions and close the loop between algorithm and repair
Requirements
- Expert knowledge of mobile fleet telemetry, especially haul trucks and shovels
- Ability to interpret multi sensor telemetry data (vibration, oil, temperature, pressure, motor current, OEM feeds)
- Experience with predictive maintenance and failure identification
- Knowledge of failure modes for engines, drivetrains, hydraulics, and structures
- Ability to ground truth and label failure events
- Experience validating model outputs against field reality
- Ability to work closely with AI and data science teams
Full Job Posting
Company Overview
- Global mining company securing critical minerals for the energy transition (copper, cobalt, nickel, tin, rare earths)
- Building an AI native platform combining IoT, artificial intelligence, and predictive intelligence across mining operations
Key Responsibilities
- Serve as the mobile fleet telemetry expert for predictive maintenance and failure identification
- Work closely with AI and data science team to translate haul truck and shovel telemetry into signals, failure modes, and features
- Interpret multi sensor and telemetry data including vibration, oil, temperature, pressure, motor current, and OEM fleet feeds
- Ground truth and label failure events so models learn from real field outcomes
- Validate model outputs against field reality, flag false alarms, and guide algorithm improvements
- Bring failure mode knowledge of engines, drivetrains, hydraulics, and structures into model design
- Partner with site teams to confirm predictions and close the loop between algorithm and repair
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