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.