Senior Applied Scientist, ML Predictive Maintenance (Asset Intelligence)
Core
Design, develop, and optimize machine learning models for fault detection and classification using vibration, OT, and time-series data to predict asset failures.
Role type
Senior Applied Scientist (ML Predictive Maintenance)
Builds
Production-grade ML models for fault detection and classification integrated into industrial work execution platforms.
Domain
Industrial IoT, Predictive Maintenance, Time-Series Analysis
Deliverable
production ML models
Required skills
Machine learning, time-series modeling, signal processing, feature engineering, Python, PyTorch, TensorFlow, statistical methods, data analysis
Preferred skills
Vibration analysis, condition monitoring, fault detection, Fourier transforms, wavelet analysis
Responsibilities
Design and optimize ML models for fault detection; perform EDA on vibration and time-series data; conduct experiments on time-series modeling and signal processing; partner with PMs on product discovery and metrics; collaborate with domain experts to validate findings; engage with peers to influence architecture decisions.
Seniority
Senior, hands-on IC