Predictive Maintenance Model for Steel Plant

I'm looking for an experienced data scientist to develop a machine learning-based predictive maintenance model. The focus will be on rolling mills and conveyor systems in steel plants, using sensor data like vibration, temperature, and speed.

Key tasks include:
- Building an LSTM autoencoder for RUL prediction
- Validating with a digital twin
- Utilizing SHAP for model interpretability

Ideal skills and experience:
- Expertise in LSTM and autoencoders
- Strong background in predictive maintenance and RUL modeling
- Experience with digital twins and SHAP
- Proficiency in Python and relevant ML libraries

Please provide a portfolio demonstrating similar projects.