Data Specialist

Caterpillar Caterpillar · Industrial · Peoria, IL

This role focuses on building and deploying predictive analytics and machine learning models, as well as AI-enabled tools and AI-driven solutions, to improve supplier quality and reduce warranty exposure. It involves data analysis, visualization, ETL processes, and supporting cloud-based environments, with a strong emphasis on translating business problems into scalable analytics solutions.

What you'd actually do

  1. Build and deploy predictive analytics and machine learning models to identify quality risks, supplier performance trends, and warranty exposure.
  2. Partner with cross-functional teams to translate business problems into statistical and machine learning approaches.
  3. Maintain and enhance business intelligence (BI) tools and dashboards to support supplier quality and warranty analytics.
  4. Prepare structured datasets for analytics, visualization, and machine learning through ETL processes.
  5. Contribute to emerging AI, automation, and Copilot-enabled solutions

Skills

Required

  • Ability to plan, execute, monitor, and manage business activities and resources to determine and achieve the actual value from a business initiative as estimated in an associated business case.
  • Demonstrates the ability to connect data analytics, data visualization, and machine learning solutions to measurable business outcomes such as quality improvement, supplier performance optimization, and reduction of warranty exposure.
  • Effective presentation tools and techniques to ensure clear understanding
  • Ability to use summarization and simplification techniques to explain complex technical concepts in simple, clear language appropriate to the audience.
  • Capable of translating advanced analytics, dashboards, and predictive modeling outputs into actionable insights for both technical and non-technical stakeholders, with an emphasis on clarity, impact, and decision enablement.
  • Knowledge of data, process, and events; ability to use tools and techniques for analyzing and documenting logical relationships among data, processes, or events.
  • Applies modeling techniques to support analytics, structured datasets, predictive modeling, and machine learning use cases, ensuring data is organized to effectively identify trends, risks, and performance opportunities.
  • Ability to collect and manage information from different sources, and distribute this information to enhance operational efficiency.
  • Supports enterprise data initiatives by managing structured and unstructured data, enabling analytics, BI, AI, and machine learning solutions that improve visibility into supplier quality and warranty performance.
  • Knowledge of cloud-based solutions and ability to design, implement, and manage them.
  • Supports analytics and machine learning workloads in cloud environments, including data storage, data pipelines, and scalable solution deployment, with the ability to troubleshoot issues and contribute to cloud-based analytics and AI initiatives.
  • Develops data model for designing an organization's database that runs effectively and efficiently for better business outcomes.
  • Participates in developing data structures that support reporting, analytics, dashboards, and machine learning datasets, ensuring performance and usability for business applications.

Nice to have

  • ETL Process

What the JD emphasized

  • machine learning
  • predictive analytics
  • AI

Other signals

  • build predictive models
  • AI-enabled tools
  • emerging AI, automation initiatives
  • machine learning models
  • AI-driven solutions