Senior Data Scientist

Caterpillar Inc.•Peoria, IL

2d•$112,710 - $183,140•Onsite

About The Position

This position will be responsible for building data driven predictive analytics models and applications using automation, artificial intelligence (AI) and machine learning (ML) models to provide actionable recommendations and insights. The position will be part of Caterpillar’s Legal, Security and Public Policy team, tackling challenges and problems that are critical to realizing business value and mitigating high impact enterprise risks. This position will work with data engineers, business analysts, and others as part of a team that assembles large, complex data sets, automates processes and pipelines, builds models and provides insight to key stakeholders.

Requirements

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.
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 & AI: Knowledge of principles, technologies and algorithms of machine learning and AI; ability to develop, implement and deliver related systems, products and services.
Completes specific tasks and initiatives utilizing machine learning and artificial intelligence technologies.
Applies specific tools in machine learning and AI in Python.
Familiarity and demonstrated use of key machine learning, artificial intelligence and agentic frameworks (scikit learn, pytorch, tensorflow, langgraph, etc.)
Programming Languages: Knowledge of basic concepts and capabilities of programming; ability to use tools, techniques and platforms in order to write and modify programming languages.
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.
Writes, debugs and implements complex queries involving multiple tables or databases.
Works with aggregate functions, complex joins, groupings, dynamic and embedded SQL's (Structured Query Languages).
Teaches others about query optimization techniques and facilities.
Consults on query optimization, interactive queries, testing and verification.
Requirements Analysis: Knowledge of tools, methods, and techniques of requirement analysis; ability to elicit, analyze and record required business functionality and non-functionality requirements to ensure the success of a system or software development project.

Nice To Haves

Masters degree, preferably in data science, statistics, economics, mathematics, or a similar field.
4+ years of experience applying SQL, Python, etc. to solve business challenges
Experience with visualization in Power BI
Strong technical and problem-solving skills and evidence of continuous learning in the analytics field
Experience leading analytics projects connecting data from various business domains
Deep Experience with Snowflake & AWS tools like S3, Lamba, EMR, Glue, Bedrock/Agent Core, etc.

Responsibilities

Demonstrate a breadth of knowledge in the application statistical methods and/or digital methods to solve business problems. Executes data gathering, data mining, and data processing of big data; creating appropriate data models.
Build and deploy statistical, machine learning, and AI models in a business context.
Coach and mentor others in the creation, validation, and application of models and digital solutions
Advocate and implement proper data governance as it applies to machine learning and AI systems.
Research and apply new methods and models to improve products and exceed customer expectations.
Focus on continual learning in the Analytics field.
Clearly explain conclusions to stakeholders with varied backgrounds and experience in quantitative analytical methods. Experience presenting and reporting business insights to management using data visualization technologies.
Optimize model performance and accuracy to improve effectiveness and business value of data analyses.