Lead Data Scientist, Gen AI for Condition Monitoring Analytics

What you'll do

• Design and implement GPU-accelerated machine learning models for anomaly detection in sensor data.
• Develop onboard digital twins using NVIDIA architecture to predict and optimize heavy machinery performance.
• Profile and tune deep learning algorithms for efficiency on NVIDIA GPUs, ensuring real-time monitoring capabilities.
• Adapt and test algorithms for onboard edge processing on Caterpillar equipment using tools like NVIDIA Jetson.
• Use high-fidelity digital twins to simulate rare failure scenarios for accurate troubleshooting with AI assistants.

What we're looking for

• Extensive experience in machine learning and advanced data analysis using Python and statistical methods.
• Deep understanding of anomaly detection, time-series analysis, and predictive maintenance models for condition monitoring.
• Proficiency in fine-tuning Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG).
• Experience handling high-frequency IoT sensor data and integrating with unified data platforms.
• Strong background in high-performance computing and GPU-accelerated algorithms on NVIDIA architecture.
• Working knowledge of cloud technologies, version control systems, and Agile development methodologies.
• Bachelor’s, Master’s, or PhD degree in a relevant technical field such as Data Science or Engineering.