Lead Data Scientist – Gen AI for Condition Monitoring Analytics

Career Area: Technology, Digital and Data Job Description: Your Work Shapes the World at Caterpillar Inc. When you join Caterpillar, you're joining a global team who cares not just about the work we do – but also about each other.  We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here – we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it. The Cat® Digital group is the digital and technology arm of Caterpillar Inc., responsible for bringing world class capabilities to our products and services. With over 1.5 million connected assets worldwide, we're focused on using data, advanced analytics, and AI capabilities to help our customers build a better world. To accomplish this, we’re deploying analytics that generate insights, recommend optimized decisions, and improve products by intelligently integrating massive quantities of telematics information, transactional records, images, unstructured documents, and other data sources.

The Analytics (Condition Monitoring) team of Cat Digital is seeking a Lead Data Scientist to be a technical expert, working in a team environment, to support the development & integration of digital twins for condition monitoring & generative AI assisted predictive analytics for Caterpillar digital applications.

Algorithm Development & Modeling Anomaly Detection: Design and implement GPU-accelerated machine learning models (e.g., XGBoost, autoencoders, and GANs) to identify irregular patterns in high-frequency sensor data. Digital Twin Engineering: Partner with engineering teams to develop onboard digital twins using NVIDIA architecture to simulate, predict, and optimize the performance of heavy machinery Optimization: Profile and tune deep learning algorithms for maximum efficiency on NVIDIA GPU architectures, ensuring high throughput and low latency for real-time monitoring. Testing onboard Architecture & Integration Edge Deployment: Adapt and test algorithms for onboard architecture, leveraging tools like NVIDIA Jetson and real-time edge processing on Cat equipment. Hardware-Software Co-Design: Collaborate with hardware / simulation engineers to ensure algorithm compatibility with next-generation processors and specialized onboard compute modules. Simulation-Based Training: Use high-fidelity digital twins to simulate rare failure scenarios, ensuring the GenAI assistant provides accurate troubleshooting steps for edge-case mechanical issues. AI Engineering Automated Diagnostic Workflows: Develop Generative AI agents that synthesize telematics data to generate prioritized repairs for identified machine faults Unified Data Orchestration: Integrate multi-modal outputs from condition monitoring analytics & asset life history to create a machine-specific context for AI assistant Work with multiple business partners in AI, digital application, product design & component groups to embed condition monitoring creating an end-to-end workflow / ecosystem Sponsor & stakeholder updates Be a technical lead on multiple complex projects with assistance of junior team members Provide a monthly status update to sponsors & stakeholders for the overall program highlighting recent achievements & next steps to deliver on the roadmap What You Will Have: Generative AI & LLMs: Proficiency in Fine-tuning and Prompt Engineering for Large Language Models, specifically using Retrieval-Augmented Generation (RAG) Condition Monitoring Algorithms: Deep understanding of Anomaly Detection, Time-Series Analysis, and Predictive Maintenance models. Telematics: Experience handling high-frequency IoT sensor data, CAN bus protocols (J1939), and integrating with unified data platforms Experience with High performance computing Business Statistics: Extensive experience with statistical