Senior Data Scientist, Cat Foresight

Caterpillar Inc.•Peoria, IL

1d•$112,710 - $183,140

About The Position

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. Cat Digital is the digital and technology arm of Caterpillar Inc., leveraging the latest technologies to build industry leading digital solutions for our customers and dealers. With over 1.5 million connected assets worldwide, our teams use data, technology, advanced analytics, telematics, and AI capabilities to help our customers build a better, more sustainable world. Join the Cat Digital Analytics team and help build the next generation of intelligent digital capabilities that power Caterpillar’s enterprise. In this role, you will design advanced analytics, AI agents, and condition‑monitoring models that turn complex equipment, telematics, and service data into actionable insights for users, customers, and internal stakeholders. You will also lead data‑driven innovation through world‑class visualization, modeling, and experimentation that directly improves fleet uptime, service efficiency, and customer value.

Requirements

Machine Learning & Intelligent Systems (Extensive): Deep understanding of ML algorithms, AI agents, LLM‑based workflows, and predictive modeling; ability to design, train, deploy, and validate advanced analytical solutions at scale.
Programming & Data Engineering (Extensive): Strong proficiency in Python (NumPy, SciPy, Pandas, scikit‑learn, PyTorch/TensorFlow optional), with experience building production‑grade data pipelines and model‑serving components.
Data Visualization & Storytelling (Extensive): Expertise in designing insightful, intuitive data visualizations using Power BI, Tableau, Plotly, or similar platforms; ability to communicate complex analytics to technical and non‑technical audiences.
Business Statistics & Analytical Thinking (Extensive): Strong statistical foundation and the ability to structure ambiguous problems, identify analytical approaches, and derive measurable, business‑relevant insights.
Query & Database Tools (Extensive): Robust experience working with large databases and cloud data environments (AWS, Snowflake); ability to extract, transform, and manipulate data efficiently.

Nice To Haves

Experience with condition monitoring, telematics analytics, or equipment health modeling.
Hands‑on experience building or integrating AI agents, LLM‑powered workflows, or autonomous decision-support systems.
Strong background in time‑series modeling, anomaly detection, asset‑health diagnostics, or predictive maintenance.
Experience deploying analytics in cloud‑native environments (AWS and Snowflake preferred; Azure acceptable).
Familiarity with MLOps, CI/CD, model monitoring, or scalable model deployment.
Certifications such as AWS Cloud Practitioner, Azure AI Engineer, AWS Machine Learning Specialty, or equivalent.
Demonstrated ability to coach and mentor junior data scientists and lead cross‑functional analytics initiatives.
Experience integrating data from diverse business domains into unified analytics solutions.

Responsibilities

Direct and optimize data gathering, mining, and processing pipelines across large-scale, multi-source datasets; develop and maintain advanced analytical and machine learning models.
Explore, develop, and implement semantic and intelligent system capabilities—such as AI agents, natural language interfaces, text analytics, and machine learning applications—to improve user experiences and scalability.
Define requirements, scope analysis efforts, and deliver actionable business insights through high-quality, intuitive data visualizations, dashboards, and analytical storytelling.
Lead research and experimentation on algorithm optimization, new modeling techniques, and analytic methods to improve accuracy, scalability, and real‑time condition monitoring performance.