Lead Data Scientist – Gen AI for Condition Monitoring Analytics

Caterpillar Inc.•Peoria, IL

3d•$128,470 - $208,770•Onsite

About The Position

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.

Requirements

Proficiency in Fine-tuning and Prompt Engineering for Large Language Models, specifically using Retrieval-Augmented Generation (RAG).
Deep understanding of Anomaly Detection, Time-Series Analysis, and Predictive Maintenance models.
Experience handling high-frequency IoT sensor data, CAN bus protocols (J1939), and integrating with unified data platforms.
Experience with High performance computing.
Extensive experience with statistical tools, processes, and practices to describe business results in measurable scales; ability to use statistical tools and processes to assist in making business decisions.
Extensive knowledge of techniques and tools that promote effective analysis; ability to determine the root cause of organizational problems and create alternative solutions that resolve these problems.
Extensive knowledge of basic concepts and capabilities of applying Python programming to solve business challenges; ability to use tools, techniques and platforms in order to write and modify programming languages.
Working knowledge of tools, methods, and techniques of requirement analysis; ability to elicit, analyze and record required business functionality and non-functionality requirements to ensure the success of a system or software development project.

Nice To Haves

Typically, a Bachelors, Masters, or PhD degree in Applied Statistics, Data Science, Business Analytics, Predictive Analytics, Business Intelligence & Analytics, Mathematics, Computer Science, Engineering (Aerospace, Electrical, Mechanical, Computer, Industrial, Agricultural, etc.), or equivalent technical degree.
Extensive experience applying Python (NumPy, SciPy, pandas, etc.) programming to solve business challenges.
Extensive experience with advanced data analysis, machine learning such as clustering, Log regressions, neural nets and statistical methods such as statistical process control, etc. (typically 8+ years).
Experience in practical applications of onboard architecture / software (e.g. mini projects using Raspberry Pi or any other architecture is a bonus).
Working experience with heavy equipment engineering or data analysis.
Working knowledge with cloud technologies (AWS, Azure, Google Cloud, etc.).
Advanced experience with version control / repositories such as GitHub.
Experience operating in an Agile environment.
Must demonstrate strong initiative, interpersonal skills, and the ability to communicate effectively.

Responsibilities

Design and implement GPU-accelerated machine learning models (e.g., XGBoost, autoencoders, and GANs) to identify irregular patterns in high-frequency sensor data.
Partner with engineering teams to develop onboard digital twins using NVIDIA architecture to simulate, predict, and optimize the performance of heavy machinery.
Profile and tune deep learning algorithms for maximum efficiency on NVIDIA GPU architectures, ensuring high throughput and low latency for real-time monitoring.
Adapt and test algorithms for onboard architecture, leveraging tools like NVIDIA Jetson and real-time edge processing on Cat equipment.
Collaborate with hardware / simulation engineers to ensure algorithm compatibility with next-generation processors and specialized onboard compute modules.
Use high-fidelity digital twins to simulate rare failure scenarios, ensuring the GenAI assistant provides accurate troubleshooting steps for edge-case mechanical issues.
Develop Generative AI agents that synthesize telematics data to generate prioritized repairs for identified machine faults.
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.
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.