Product Engineering: Lead Product Leader - PxE Talent

Product Engineering: Lead Product Leader - PxE Talent

Engineering and Product | Product Strategy and Management

Same job available in 5 locations

Dallas, Texas, United States Hermitage, Tennessee, United States Jersey City, New Jersey, United States Morristown, New Jersey, United States New York, New York, United States

An Applied AI Lead Product Manager is an expert individual contributor responsible for ensuring a product’s value and viability within a product line. This role involves leading empowered, cross-functional product teams to solve complex customer problems that align with strategic business needs. The Applied AI Lead Product Manager is accountable for the product’s success, from vision to execution, and collaborates closely with various functions and stakeholders to deliver valuable, viable, usable, and feasible solutions. As an expert in the craft, this role sets the standard for harnessing AI and orchestrating agentic tools to compress the concept-to-cash learning loop—automating analysis, prototyping, and compliance detail-work so the team can focus on the human judgment AI cannot replace: product sense-making. The Applied AI Lead Product Manager plays a crucial role in ensuring the success of our most strategic, complex products by balancing customer needs with business objectives. This role requires a blend of strategic vision, analytical skills, and collaborative teamwork to deliver valuable, viable, usable, and feasible solutions. It demands a high degree of experience and expertise in the modern product management craft and a drive for continuous improvement—amplified by the fluent, responsible use of AI to learn faster and earn faster. Work you’ll do Product Accountability Responsible and accountable for the product's value and viability showcasing a measurable Return on Investments (ROI) Drive strategy-aligned solutions to achieve product value objectives. Formulate and achieve Key Performance Indicators (KPIs) for identified problems to solve. Measure KPIs and analyze outcomes to inform future strategies. Leverage AI to harvest outcome evidence early and often, lowering total cost of ownership (TCO). Vision and Strategy Co-create, own, and evangelize the product vision, strategy, and roadmap, using AI to deepen domain knowledge and simulate future scenarios to chart pathways others have not yet seen. Align product objectives with the product line and business goals. Co-create in collaboration with business stakeholders, engineering, experience, and delivery. Market and User Engagement Conduct user research and competitive analysis, using AI agents to synthesize research at speed—accelerating the data crunching, ensuring the human connection. Engage the team with users and stakeholders through continuous research and direct interactions. Collaborate and guide the team toward solutions that address priority user and business needs. Collaboration and Teamwork Work side-by-side with cross-functional (business, engineering, experience, and delivery) team members to achieve KPI outcomes. Promote a product operating model that emphasizes outcomes over output (minimize overproduction while maximizing value). Build empowered teams and product communities who exhibit collective product ownership. Continuous Improvement Remove obstacles for the team and ensure smooth flow of continuous value achievement. Promote and drive rapid, emergent, and ongoing learning and adaptation to meet objectives. Drive innovation and improvement of the process to drive out waste and accelerate value achievement, using AI as a force-multiplier to offload the repetitive, speed up the sluggish, and automate the mundane. Spread knowledge and best practices within the product vertical community. Applied AI Ways of Working Amplify innovation: use AI to rapidly deepen domain knowledge, surface untapped market and user potential, and simulate future scenarios—charting new pathways for the