Principal Digital Product Manager, Applied AI

Caterpillar•Chicago, IL

2d•$156,000 - $253,560

About The Position

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 Summary: The AI Accelerator is a key enabler of Caterpillar’s next chapter — designed to enable and scale AI adoption and progress across the enterprise. Its mission is to identify opportunities, run experiments, accelerate adoption, ensure AI value realization, support strategic partner engagement, and help develop the frameworks to scale AI solutions globally. We are building a team of experienced and visionary Applied AI Product Managers to lead the strategy, development, and launch of AI capabilities across the enterprise. This unique role sits at the intersection of business strategy, technical AI/ML capabilities, and user experience. The ideal candidate will have a deep understanding of AI principles, a passion for solving user problems, and a proven ability to guide technical teams in bringing complex AI capabilities from the lab to production.

Requirements

Strategic Vision: Ability to look beyond current constraints and define a compelling long-term AI product strategy.
Data-Driven Decision Making: Relies heavily on metrics, data, and experimentation to guide product prioritization.
Bias for Action: Proactive and results-oriented, with a track record of successfully shipping complex products.
Cross-functional Collaboration: A natural collaborator who can build consensus and drive outcomes across diverse engineering, data science, and business teams.
Extensive professional experience (typically 5+ years), with proven experience as a Product Manager focused on Machine Learning, Deep Learning, or AI-powered products (typically 3+ years).
A solid foundational understanding of the ML lifecycle, common model types (e.g., NLP, Computer Vision, Predictive Models), feature engineering, and the challenges of deploying and monitoring models in production environments (MLOps) .
Exceptional written and verbal communication skills, with the ability to articulate complex AI concepts to non-technical business leaders and users, as well as define detailed requirements for technical teams.
Proven ability to define and execute a product roadmap based on quantitative data, market analysis, and user research.
Typically a Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field.

Nice To Haves

Direct experience or degree in Data Science or Engineering
Experience with cloud-based ML platforms (e.g., AWS Sagemaker, Google AI Platform, Azure ML)
Prior experience managing products in a specific industry (e.g., FinTech, Healthcare, E-commerce)
Familiarity with A/B testing frameworks for evaluating and improving model-driven features

Responsibilities

Define AI Product Strategy & Roadmap: Partner with key business functions to define the product vision, strategy, and roadmap for one or more AI-driven product areas. Identify high-impact opportunities where applied AI can deliver significant business value and exceptional user experiences.
Translate Insights to Requirements: Work closely with stakeholders, business analysts, and users to understand needs. Translate these needs into clear, concise Epics, User Stories, and PRDs (Product Requirements Documents) that specifically address the unique constraints and capabilities of AI/ML systems.
Lead the AI Development Lifecycle: Collaborate daily with Data Scientists, ML Engineers, and Software Engineers to manage the end-to-end product development lifecycle, including data acquisition, model training, model deployment, and production monitoring ( MLOps ).
Manage Model Performance & Metrics: Define and monitor key product and model performance metrics (e.g., accuracy, precision, recall, latency, drift). Clearly communicate trade-offs between model performance and business objectives.
Launch, Adoption & Value: Partner with functional teams across the enterprise to successfully launch new AI products or features, ensuring readiness and clear value proposition and realization.
Data Ethics and Compliance: Advocate for and ensure that AI products are developed and deployed ethically, complying with all relevant data privacy, bias, and regulatory requirements.