Senior Data Scientist

Career Area: Technology, Digital and Data Job Description: 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.

Definition Senior Data Scientist to design, develop, and deploy advanced AI/ML solutions that drive business impact. The ideal candidate will possess deep expertise in statistics, machine learning, large language models (LLMs), model evaluation, and Python-based solution development. This role will partner closely with product, engineering, and business stakeholders to develop scalable analytics and Generative AI solutions that improve decision-making, automation, and customer experiences. Responsibilities • Apply advanced statistical techniques to analyze large-scale structured and unstructured datasets, identify insights, and quantify business impact. • Develop end-to-end ML and GenAI pipelines, including data preparation, model training, fine-tuning, evaluation, deployment, and monitoring. • Build scalable Python-based solutions and reusable ML components that integrate with enterprise platforms and cloud environments. • Collaborate with cross-functional teams to translate business problems into analytical solutions and communicate findings to technical and non-technical stakeholders. • Stay current with emerging AI, machine learning, and LLM advancements, driving innovation and adoption of best practices.

Descriptors 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. Level Working Knowledge: • Explains the basic decision process associated with specific statistics. • Works with basic statistical functions on a spreadsheet or a calculator. • Explains reasons for common statistical errors, misinterpretations, and misrepresentations. • Describes characteristics of sample size, normal distributions, and standard deviation. • Generates and interprets basic statistical data. Accuracy and Attention to Detail: Understanding the necessity and value of accuracy; ability to complete tasks with high levels of precision. Level Extensive Experience: • Evaluates and makes contributions to best practices. • Processes large quantities of detailed information with high levels of accuracy. • Productively balances speed and accuracy. • Employs techniques for motivating personnel to meet or exceed accuracy goals. • Implements a variety of cross-checking approaches and mechanisms. • Demonstrates expertise in quality assurance tools, techniques, and standards. 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. Level Working Knowledge: • Approaches a situation or problem by defining the problem or issue and determining its significance. • Makes a systematic comparison of two or more alternative solutions. • Uses flow charts, Pareto charts, fish diagrams, etc. to disclose meaningful data patterns. • Identifies the major forces, events and people impacting and impacted by the situation at hand. • Uses logic and intuition to make inferences about the meaning of the data and arrive at conclusions. Machine Learning: Knowledge of principles, technologies and algorithms of machine learning; ability to develop, implement and deliver related systems, products and services. Level Working Knowledge: • Completes specific tasks and