Data Science Pod Lead - Autonomy Engineer

Caterpillar Inc.•Irving, TX

19h•$97,530 - $158,480•Onsite

About The Position

The Data Science Pod Lead is responsible for guiding a multidisciplinary team of data scientists, machine learning engineers, and analysts through the design, development, validation, and deployment of data science and AI solutions. This role translates product and business objectives into executable technical plans, ensures adherence to best practices and responsible AI principles, and mentors pod members to build strong technical and professional capabilities. The Pod Leader works closely with AI Product Owners, AI Architects, engineering partners, and business stakeholders to deliver measurable value. Within the Agentic Services & Metrics pod, this role combines technical leadership, AI enablement, and data-driven decision making to accelerate adoption of AI-assisted engineering tools across the organization. The Pod Lead will partner closely with users, and product stakeholders to optimize how AI tooling is integrated into daily development workflows, measure business impact through adoption and productivity metrics, and help shape the future direction of enterprise AI enablement initiatives as usage continues to scale.

Requirements

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 sufficient to collaborate effectively with engineering teams; hands-on coding is not a primary responsibility.
Software Development Life Cycle: Knowledge of software development life cycle; ability to use a structured methodology for delivering and managing new or enhanced software products to the marketplace.
Agile Product Ownership: Experience working in Agile teams with hands-on ownership of product backlogs and sprint planning.
Describes similarities and differences of life cycle for new product development vs. new release.
Identifies common issues, problems, and considerations for each phase of the life cycle.
Works with a formal life cycle methodology.
Explains phases, activities, dependencies, deliverables, and key decision points.
Interprets product development plans and functional documentation.
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.
AI Product Acumen: Working knowledge of AI/ML and Generative AI concepts sufficient to make informed product trade-off decisions (not expected to design or code models).
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.
Technical Troubleshooting: Knowledge of technical troubleshooting approaches, tools, and techniques; ability to anticipate, recognize, and resolve technical issues on hardware, software, application, or operation.
Problem-Solving: Strong problem-solving skills, with the ability to think critically and creatively to develop innovative solutions.
Identifies and documents specific problems and resolution alternatives.
Examines a specific problem and understands the perspective of each involved stakeholder.
Develops alternative techniques for assessing accuracy and relevance of information.
Helps to analyze risks and benefits of alternative approaches and obtain decision on resolution.
Uses fact-finding techniques and diagnostic tools to identify problems.
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.

Nice To Haves

AI Product Acumen: Working knowledge of AI/ML and Generative AI concepts sufficient to make informed product trade-off decisions (not expected to design or code models).
Data-Informed Decision Making: Ability to interpret analytics, experiments, and model performance metrics to guide prioritization and roadmap decisions.
Ability to translate business strategy into outcome-driven product goals and measurable value hypotheses.
Agile Product Ownership: Experience working in Agile teams with hands-on ownership of product backlogs and sprint planning.
Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.

Responsibilities

Lead the technical direction, execution, and continuous evolution of the Agentic Services & Metrics pod, ensuring delivery of scalable AI enablement and analytics solutions.
Drive adoption of AI-assisted engineering tools by developing onboarding strategies, implementation approaches, and best practices that improve developer productivity and software delivery outcomes.
Partner with users and engineering teams to deploy, integrate, troubleshoot, and optimize AI tooling throughout the software development lifecycle.
Lead pod-level planning and execution, balancing user enablement, operational support, analytics, experimentation, and delivery commitments.
Report on actionable insights that help teams understand AI tool effectiveness, user engagement, and productivity outcomes
Provide technical leadership, coaching, and mentorship across data science, machine learning, GenAI, and software engineering disciplines.
Stay current with advancements in agentic AI, developer productivity tooling, and AI-assisted software engineering, helping define future services and capabilities as adoption expands.