Lead Data Scientist (Service Events & Insights)

Caterpillar•Peoria, IL

5d•Onsite

About The Position

Our Product Support and Logistics Division (PSLD) is one of the most innovative divisions within Caterpillar. In PSLD, as within all of Caterpillar, our priority is our customers and helping them build a better world. We touch nearly every element of the services value stream. PSLD is responsible for Aftermarket Parts Distribution of Cat Parts, Integrated Logistics and Global Services supporting Caterpillar dealers and customers. Our teams work together in critical areas that leverage digital capabilities, technology and operational excellence. The Global Service organization within PSLD provides world-class support to Cat® dealers across the globe. The Lead Data Scientist – Service Events & Insights will perform analytical tasks and initiatives on huge amount of data to support data-driven business decision and development.

Requirements

Accuracy and Attention to Detail: Understanding the necessity and value of accuracy; ability to complete tasks with high levels of precision.
Analytical Thinking: Knowledge of techniques and tools that promote effective analysis; ability to determine the root cause of organizational problems and create alternative solutions that resolve these problems.
Machine Learning: Knowledge of principles, technologies and algorithms of machine learning; ability to develop, implement and deliver related systems, products and services.
Programming Languages: Knowledge of basic concepts and capabilities of programming; ability to use tools, techniques and platforms to write and modify programming languages.
Business Statistics: Knowledge of the statistical tools, processes, and practices to describe business results in measurable scales; ability to use statistical tools and processes to assist in making business decisions.
Query and Database Access Tools: Knowledge of data management systems; ability to use, support and access facilities for searching, extracting and formatting data for further use.

Nice To Haves

4-year college degree in Business, Engineering, Data Science, Mathematics, or a related field, or equivalent experience
Deep expertise in statistical analysis and machine learning techniques, with the ability to apply advanced methodologies to solve complex business problems
Strong proficiency in programming languages (e.g., Python, R, SQL) and data processing frameworks, with experience developing and productionizing analytical solutions
Advanced knowledge of data modeling, database systems, and query optimization, including experience working with large-scale, distributed datasets
Demonstrated ability to lead end-to-end machine learning initiatives, including model selection, training, evaluation, and performance monitoring
Proven expertise in translating complex analytical findings into clear, actionable insights for both technical and non-technical audiences
Strong analytical thinking and problem-solving capabilities, including the ability to decompose complex challenges and evaluate multiple solution paths
Experience with Service Events Domain data, including dealer Work Orders, SMCS hierarchies, service response, fluid sampling, inspection, and Cat Interact to drive practical, customer-focused outcomes
Ability to coach, mentor, and influence others in data science best practices, raising the overall technical capability of the organization
Demonstrated ability to balance analytical rigor with business impact, ensuring solutions are both technically sound and commercially viable

Responsibilities

Leading the design and execution of advanced analytics and data science initiatives, translating large-scale Service Events data into actionable business insights
Directing data acquisition, transformation, and modeling efforts across complex, high-volume datasets to enable scalable and reliable analytics solutions
Defining problem statements, analytical approaches, and success criteria in collaboration with business stakeholders to ensure alignment with strategic objectives
Developing, deploying, and optimizing machine learning models and statistical analyses to improve service outcomes, forecasting, and decision-making accuracy
Applying advanced techniques such as natural language processing, text analytics, and predictive modeling to extract value from structured and unstructured service data
Leading investigation into data model optimization, algorithm performance, and analytical methodologies to continuously improve solution effectiveness
Communicating insights and recommendations through data visualization and storytelling, enabling informed decision-making at multiple levels of the organization
Serving as a technical thought leader, guiding best practices in data science, analytics, and experimentation across cross-functional teams