Senior Data Scientist

Caterpillar•Peoria, IL

17h•$112,710 - $183,140•Onsite

About The Position

Your Work Shapes the World at Caterpillar Inc. 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. At Caterpillar, the impact of data goes far beyond dashboards. As a Senior Data Scientist, you’ll help solve complex, real‑world problems that influence how we procure, plan, and operate on a global scale. Your work will turn data into insights leaders trust and use to make better decisions. This role is ideal for an experienced, hands‑on data scientist who enjoys working end‑to‑end, from shaping analytical approaches, to building robust models, to ensuring solutions are adopted and deliver real business value. You’ll partner closely with procurement, finance, digital, and operations teams to bring analytics into production.

Requirements

Strong foundation in machine learning and statistical modeling, including model development, validation, and monitoring.
Proficiency in Python and SQL for analytics, data pipelines, and model development.
Experience working with cloud‑based analytics platforms and production data environments.
Ability to translate complex analytical concepts into clear, business‑focused insights.
Experience building decision‑oriented dashboards and visualizations.

Nice To Haves

Experience operationalizing machine learning solutions, including MLOps, CI/CD pipelines, and model monitoring.
Strong SQL and Snowflake experience supporting ETL and analytics datasets.
Hands‑on experience with techniques such as time series, clustering, tree‑based models, GLMs, or neural networks.
Experience with Large Language Models (LLMs) or Natural Language Processing (NLP) use cases.
Familiarity with data governance, enterprise analytics environments, and value realization.
Proven ability to drive business adoption and ROI from analytics solutions.

Responsibilities

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.
Develop, validate, and maintain predictive models using machine learning and statistical methods aligned to business needs.
Build and manage analytics‑ready datasets by designing ETL pipelines and integrating data sources (SQL, Snowflake).
Create executive‑ready dashboards and decision tools (Power BI) that support data‑driven leadership decisions.
Partner cross‑functionally with Procurement, Finance, Digital/IT, and Operations to deploy and operationalize analytics solutions.
Apply data governance and operational best practices to ensure reliable, scalable analytics.
Use modern engineering practices (Git, CI/CD, MLOps) to improve solution quality and sustainability.
Mentor and guide others on analytical and technical approaches.