Senior Product Manager, Consumer

Work Truck Solutions is the operating system for the commercial vehicle industry, connecting OEMs, upfitters, dealerships, and fleet buyers through a digital platform. The Senior Product Manager will own the Consumer shopping experience end to end, from strategy and discovery through execution and measurement, while using customer research, analytics, AI, and ML to improve complex commercial vehicle workflows.

Responsibilities:

• Own strategy, roadmap, and execution for our Consumer shopping experience, including the tradeoffs required to keep it focused
• Develop deep, first-hand understanding of dealer, upfitter, OEM, and fleet buyer workflows through customer conversations, ride-alongs, support data, and usage analytics
• Write crisp problem definitions, specs, and success criteria that give engineering and design room to solve rather than instructions to follow
• Define and instrument the metrics that matter for your area; run the analysis yourself rather than waiting on a request queue
• Partner with design on discovery and validation, and with engineering on sequencing, scope, and technical tradeoffs
• Identify where AI and ML meaningfully improve the product—data enrichment, search and matching, workflow automation, decision support—and scope those bets with honest evaluation criteria and clear failure modes
• Drive launches end to end: enablement for sales and support, customer communication, adoption tracking, and iteration after release
• Communicate up and out: keep executives, GTM teams, and customers aligned on what's coming, what changed, and why
• Contribute to the broader product operating model—discovery practices, prioritization frameworks, and how the team makes decisions

Requirements:

• 6+ years in product management, with at least one area or product line you owned end to end and can speak to in detail—including what didn't work
• Demonstrated experience with complex B2B or marketplace products, ideally multi-sided, where different user groups have conflicting incentives
• Strong discovery instincts: you talk to users regularly, you distinguish what they ask for from what they need, and you kill your own ideas when the evidence says to
• Excellent written communication; your specs and updates reduce ambiguity rather than add to it
• Track record of shipping meaningful outcomes in an Agile environment, and comfort operating without perfect information
• Self-sufficient with data: you write your own SQL or work fluently in your analytics stack, build your own dashboards, and don't outsource your understanding of your own metrics
• Comfortable with experimentation and measurement—choosing the right metric, sizing impact honestly, and knowing when an A/B test isn't the right tool
• Enough technical depth to hold a real conversation about APIs, data models, integrations, and system constraints, and to earn engineering's respect in design review
• Experience with data-heavy products where the quality and structure of the underlying data is itself the customer problem
• Has shipped AI or ML features in production and can explain the tradeoffs made—not just the launch, but the evaluation approach, the failure cases, and what you did about them
• Practical understanding of what current AI is and isn't good at, and the judgment to say no to AI features that create more customer risk than value
• Comfortable defining quality bars for probabilistic systems: what accuracy is good enough, how errors surface to users, what the fallback is, and how you monitor degradation over time
• Understands the economics—inference cost, latency, human-in-the-loop review—and can build a business case that survives contact with those numbers
• Fluent with AI tooling in your own workflow (research synthesis, prototyping, analysis, drafting) and able to raise the team's practice without overselling it
• Able to partner credibly with data science and ML engineering: you can scope a model problem, interpret an evaluation, and push back when a metric doesn't reflect the user's reality
• High ownership and low ego; you'll do the unglamorous work when it's what the customer needs
• Demonstrated influence without authority across engineering, design, sales, and support
• Bias toward shipping and learning over exhaustive planning, paired with the discipline to follow through past launch
• Bachelor's degree or equivalent practical experience
• Currently resides in one of the following states: CA, TX, FL
• Experience in automotive, dealership software, logistics, supply chain, fleet, or industrial B2B
• Background with catalog, taxonomy, configuration, or inventory data products
• Experience introducing AI capabilities into an existing product rather than building AI-native from scratch
• Comfort with field research in non-office environments—dealerships, upfit shops, service bays