Data Scientist I, Lens Customer Analytics

Caterpillar•Peoria, IL

4d•Onsite

About The Position

Join Caterpillar’s Customer Analytics and Applied AI team within Cat Digital to perform analytical tasks and initiatives on large volumes of data in support of data-driven business decisions and development. The Lens solution uses historical customer and dealer data to identify real-world customer behavior and unmet needs, helping support customers proactively. The role involves gathering, mining, and processing large volumes of data; creating data models; applying natural language processing, text analysis, and machine learning to develop semantic data capabilities; defining analysis requirements and scope; communicating business insights to management using data visualization; and researching data-model optimization and algorithms to improve analytical effectiveness and accuracy. The position is based in Caterpillar’s Peoria, Illinois office. Relocation assistance and visa sponsorship are not available. The listed salary range is $89,210 to $144,960. Compensation and benefits may vary based on individualized factors. Employment offers are conditioned on successful completion of a drug screen.

Requirements

Working knowledge of statistical tools, processes, and practices for describing business results and supporting business decisions.
Working understanding of the need for accuracy and the ability to complete tasks with a high level of precision.
Working knowledge of analytical techniques and tools, including identifying root causes and developing alternative solutions.
Basic knowledge of machine-learning principles, technologies, and algorithms, and the ability to develop, implement, and deliver related systems, products, or services.
Working knowledge of developing with AI agents and GitHub Copilot.
Basic knowledge of Python programming and the ability to use tools, techniques, and platforms to write and modify code for business challenges.
Working knowledge of data management systems and tools for searching, extracting, and formatting data.
Working knowledge of requirements-analysis tools and methods, including eliciting, analyzing, and documenting functional and non-functional requirements.
Typically, a master’s or Ph.D. in a relevant technical field or equivalent technical degree, with 2+ years of experience.
Extensive experience using Python, including NumPy, SciPy, and pandas, to solve business challenges.
Typically 1+ years of extensive experience with advanced data analysis and statistical methods such as regression, hypothesis testing, ANOVA, and statistical process control.
Extensive practical experience with machine-learning techniques such as clustering, logistic regression, random forests, SVM, or neural networks.
Extensive experience with AI tools and agentic development, such as LangChain, Bedrock, and GitHub Copilot.
Advanced experience quantifying costs, benefits, risks, and chances of success before recommending a course of action.
Working experience with customer analytics.
In-depth technical and problem-solving skills and evidence of continuous learning in analytics.
Working knowledge of cloud technologies such as AWS, Azure, or Google Cloud.
Advanced experience with version control and repositories such as GitHub.
Experience operating in an Agile environment.
Strong initiative and interpersonal skills, with the ability to communicate effectively.

Nice To Haves

Experience designing or developing analytics for heavy equipment industries, such as automotive, aerospace, engines, transmissions, construction, mining, or industrial equipment.

Responsibilities

Gather, mine, and process large volumes of data and create appropriate data models.
Explore, promote, and implement semantic data capabilities using natural language processing, text analysis, and machine learning techniques.
Define requirements and scope for data analyses.
Present and report potential business insights to management using data visualization technologies.
Research data-model optimization and algorithms to improve the effectiveness and accuracy of data analyses.