Senior Product Manager, Consumer

Senior Product Manager, Consumer

About Work Truck Solutions

The Opportunity

Key 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.

Qualifications

• 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

Nice to Have

• 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.

Why Join Us?

• Remote Flexibility: Work from anywhere in the U.S. while staying connected to a collaborative team.
• Impactful Work: Contribute to products that are reshaping the commercial vehicle industry.
• Growth Opportunities: Be part of a rapidly growing company with ample opportunities for professional development.
• Inclusive Culture: Join a team that values diversity, creativity, and innovation.