Career Area:
Engineering
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.
Job Description Summary
The Data Science Pod Member is an individual contributor responsible for designing, developing, validating, and deploying data science and AI solutions as part of a multidisciplinary pod. This role focuses on hands-on analytical, modeling, and engineering work, translating business and product objectives into high‑quality technical deliverables. Pod Members collaborate closely with the Data Science Pod Lead, AI Product Owner, AI Architects, and engineering partners to deliver measurable value while adhering to enterprise standards and Responsible AI principles.
Role Definition
Responsibilities
Data Science & Solution Development
- Design, build, test, and iterate on data science, machine learning, and Generative AI (GenAI) solutions aligned to pod objectives, including LLM‑based systems, retrieval‑augmented generation (RAG), agents, and multimodal use cases.
- Perform data exploration, feature engineering, model training, evaluation, and validation, including GenAI‑specific evaluation (e.g., groundedness, hallucination risk, latency, cost, and quality metrics).
- Implement solutions that are scalable, maintainable, and aligned with enterprise architecture, data, and engineering standards, with explicit consideration for GenAI safety, security, and Responsible AI controls (prompt management, guardrails, data provenance, and access controls).
- Contribute production‑ready code, notebooks, pipelines, and model artifacts, including prompts, system instructions, evaluation harnesses, and GenAI configuration assets.
Delivery & Execution
- Execute assigned work items to meet sprint and increment commitments aligned to the product roadmap.
- Balance experimentation with delivery, supporting the transition from proof‑of‑concept to production.
- Identify and communicate technical risks, assumptions, data limitations, and trade‑offs to the Pod Lead.
- Support operational readiness through testing, documentation, and handover activities.
Collaboration & Ways of Working
- Work closely with the Data Science Pod Lead to align technical work with pod‑level direction and priorities.
- Partner with AI Product Owners to understand business problems, success metrics, and value hypotheses.
- Collaborate with platform, data engineering, MLOps, and software engineering teams.
- Communicate analytical findings, model behavior, and recommendations to both technical and non‑technical stakeholders.
Quality, Governance & Responsible AI
- Ensure models and analytics meet quality, performance, security, reliability, and compliance standards.
- Apply Responsible AI principles throughout the solution lifecycle.
- Produce and maintain appropriate technical documentation, experiments, and traceability artifacts.
Continuous Improvement & Innovation
- Stay current with advances in data science, ML, and Generative AI techniques.
- Contribute ideas to improve tools, processes, and reusable assets across the data science practice.
- Participate in communities of practice, knowledge sharing, and peer reviews
Degree Requirement
Bachelor’s degree in engineering, computer science, data science, mathematics, statistics, or a related field (or equivalent practical experience). Advanced degree (Master’s or PhD) in artificial intelligence, machine learning, engineering, mathematics, physics, or a closely related field is considered an advantage.
Skill Descriptors
- Data Science & ML Foundations :Hands‑on experience applying statistical analysis, machine learning, and/or Generative AI techniques to real‑world problems.
- Programming & Tooling: Proficiency in relevant programming languages and tools (e.g., Python, SQL, notebooks, ML frameworks); ability to write, test, debug, and maintain production‑quality code.
- Data‑Informed Problem Solving: Ability to analyze data, experiments, and model performance metrics to generate insights and guide technical decisions.
- Agile Delivery: Experience working in Agile teams, contributing to sprint planning, estimation, and iterative delivery.
- Communication & Collaboration: Clear verbal and written communication skills, with the ability to explain technical concepts and analytical results to diverse audiences..
Level Working Knowledge
- Identifies and documents specific problems and potential analytical or modeling approaches.
- Examines problems from multiple stakeholder perspectives.
- Develops and evaluates alternative techniques for assessing accuracy, relevance, and performance.
- Uses appropriate fact finding techniques, diagnostics, and experimentation
Software Development Life Cycle
- Knowledge of the software development life cycle
- Ability to work within a structured methodology for delivering and maintaining data science and AI solutions, including development, testing, deployment and support
Artificial Intelligence
- Knowledge of AI and Generative AI concepts, risks, and opportunities; ability to govern and guide AI product development to achieve business outcomes while adhering to responsible AI principles.
- Explains the methodology and technologies of artificial intelligence.
- Describes the concepts, functions and features of artificial intelligence (AI).
- Locates relevant resources to obtain the latest information on artificial intelligence.
- Cites examples of successful implementation of AI technologies and systems.
Programming
Knowledge of relevant programming languages and tools; ability to test, write, design, debug, troubleshoot, and maintain source codes and computer programs.
- Prompt engineering
- Programming literacy in AI related languages (eg. Python, R, etc)
Technical Troubleshooting
Knowledge of technical troubleshooting approaches, tools, and techniques; ability to anticipate, recognize, and resolve technical issues on hardware, software, application, or operation.
- Discovers, analyzes, and resolves hardware, software, or application problems.
- Works with vendor-specific diagnostic guides, tools, and utilities.
- Handles calls related to product features, applications, and compatibility standards.
- Analyzes code, logs, and current systems as part of advanced troubleshooting.
- Records and reports specific technical problems, solving processes, and tools that have been used.
Note:
This Job Description is intended as a general guide to the job duties for this position and is intended for the purpose of establishing the specific salary grade. It is not designed to contain or be interpreted as an exhaustive summary of all responsibilities, duties, and effort required of employees assigned to this job. At the discretion of management, this description may be changed at any time to address the evolving needs of the organization. It is expressly not intended to be a comprehensive list of “essential job functions” as that term is defined by the Americans with Disabilities Act.
Posting Dates:
March 26, 2026 - April 8, 2026
Caterpillar is an Equal Opportunity Employer. Qualified applicants of any age are encouraged to apply
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