Senior Data Scientist

Caterpillar · Industrial · Peoria, IL

Senior Data Scientist at Caterpillar responsible for leading analytics initiatives, developing and validating predictive models using ML/AI, building analytics-ready datasets, creating dashboards, and operationalizing analytics solutions. The role involves partnering with various business units to deploy and adopt these solutions, ensuring data governance and using modern engineering practices. Experience with MLOps, LLMs/NLP, and driving business adoption is a plus.

What you'd actually do

  1. Lead analytics initiatives by translating business problems into analytical approaches, models, and actionable insights. Initiatives include critical cost analytics solutions and, S2C and P2P solutions using ML / AI or any relevant solution.
  2. Develop, validate, and maintain predictive models using machine learning and statistical methods aligned to business needs.
  3. Build and manage analytics‑ready datasets by designing ETL pipelines and integrating data sources (SQL, Snowflake).
  4. Create executive‑ready dashboards and decision tools (Power BI) that support data‑driven leadership decisions.
  5. Partner cross‑functionally with Procurement, Finance, Digital/IT, and Operations to deploy and operationalize analytics solutions.

Skills

Required

  • machine learning
  • statistical modeling
  • model development
  • model validation
  • model monitoring
  • Python
  • SQL
  • analytics
  • data pipelines
  • model development
  • cloud-based analytics platforms
  • production data environments
  • translate complex analytical concepts into clear, business-focused insights
  • building decision-oriented dashboards and visualizations

Nice to have

  • operationalizing machine learning solutions
  • MLOps
  • CI/CD pipelines
  • model monitoring
  • Snowflake
  • ETL
  • time series
  • clustering
  • tree-based models
  • GLMs
  • neural networks
  • Large Language Models (LLMs)
  • Natural Language Processing (NLP)
  • data governance
  • enterprise analytics environments
  • value realization
  • drive business adoption
  • ROI

What the JD emphasized

  • from concept through deployment and adoption
  • moderately complex to complex problems
  • formal education, equivalent professional experience, or a combination of both

Other signals

  • Develop, validate, and maintain predictive models using machine learning and statistical methods aligned to business needs.
  • Partner cross-functionally with Procurement, Finance, Digital/IT, and Operations to deploy and operationalize analytics solutions.
  • Experience operationalizing machine learning solutions, including MLOps, CI/CD pipelines, and model monitoring.