Top Entry Level Product Manager Jobs in Minneapolis, MN

Own the lifecycle, quality, delivery, pricing, packaging, and commercialization of vertical data products. Partner with customers, sales, analysts, engineering, legal, pricing, and marketing to build data-driven workflows, prioritize investments, execute strategic deals, and increase revenue. Serve as the subject matter expert for specialized datasets, ensuring strong coverage, accuracy, entity resolution, platform integration, and customer adoption.

As the Founding Product Manager at Freeflow, you will define AI-driven products for the insurance industry, focusing on claims auditing solutions, enhance efficiency and collaborate closely with teams and users to translate pain points into deliverables.

Own the end-to-end Service Experience product strategy and roadmap for a two-sided home-services marketplace. Lead AI-powered customer and Pro support, evaluation systems, trust and conflict-resolution policies, and AI-driven experience innovation. Own Contribution LTV and brand-promise metrics, prototype with LLMs, analyze transcripts and data, and influence senior leaders and cross-functional peers without direct reports. Success includes scaling high-quality AI resolution, improving profitability and service outcomes, and delivering an AI experience proof of concept.

Own product direction for an enterprise AI platform, including requirements, roadmap, acceptance criteria, scope, and stage-gate processes. Translate customer and stakeholder needs into prioritized, testable requirements; manage trade-offs among value, velocity, leverage, and technical risk; and partner with engineering on delivery decisions. Recruit design partners, validate customer needs, document decisions, communicate with executives and clients, and maintain disciplined delivery across multiple product teams.

Owns the end-to-end product strategy and execution for AI-powered systems. Defines requirements, evaluation frameworks, quality standards, and system behavior while balancing quality, latency, cost, reliability, safety, and user experience. Partners closely with ML, backend, and client engineering teams to identify failure modes, improve model performance, prioritize roadmaps, and deliver predictable, useful, trusted AI experiences.