Senior Product Manager, Consumer

We need to output HTML fragment with sections: About (maybe "About the role"? Actually they said use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). We need to include sections only when posting has real content. We have: About Work Truck Solutions (company description) and The Opportunity (role description). Could map to "About the role" maybe combine? The posting includes "About Work Truck Solutions" and "The Opportunity". We could treat "About the role" as the opportunity description. Also we have "Key Responsibilities" (list" (Responsibilities). Then "Qualifications" with sub-sections: Product craft, Data and technical fluency, AI and ML product capability, Working style. Could combine into Qualifications. Also "Nice to Have" maybe part of Qualifications? Could include as part of Qualifications but maybe separate Skills? They said Skills section optional. We have "Why Join Us?" which could be Benefits? Actually they list Remote Flexibility, Impactful Work, Growth Opportunities, Inclusive Culture. Those are benefits/perks. So we can create Benefits section. Pay: Starting Pay Range: $155k - $172k/yr. So we need Pay section. Schedule: Not mentioned. So omit Schedule. Location: Remote (U.S.-based) Preference given to CA, TX and FL. Also "Currently resides in one of the following states: CA, TX, FL". That's location requirement. Could be part of Qualifications? But they said do not put Job ID etc. Could include location in a short p maybe under a heading? There's no heading for Location. Could embed in a p after the heading? But they said only include sections when posting has real content. There's no explicit Location heading, but we could include location info in a p under maybe "About the role"? However they said do not repeat job title as heading. Not about location. I'd include location info in a p under maybe "About the role" or as a separate short p before sections? The instruction: "fold minor metadata into a short

or drop it." So we can include location as a short p before sections. Thus output:

Location: Remote (U.S.-based) with preference for candidates residing in CA, TX, or FL.

Then sections:

About the role

with content from The Opportunity paragraph(s). Combine. Then

Responsibilities

with

• each responsibility. Then

  Qualifications

  with maybe sub-sections? They didn't ask for subheadings inside Qualifications; just use

  for prose and

  • for lists. Could break into multiple

    paragraphs for each bullet group. But we could also use

    • for each qualification group? However they said use
      • for any list of duties/requirements/benefits. So qualifications are requirements; we can list them as
        • . But there are multiple subcategories; we could still list each bullet as a requirement. Might be long but okay. We need to preserve all substantive information. So we need to include each qualification point. Let's extract qualifications: Product craft: - 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 Data and technical fluency: - 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 AI and ML product capability: - 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 Working style: - 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 (could be included as part of Qualifications or separate Skills). Since they said Skills section optional, we could put Nice to Have under Skills. But they said use
          • for any list of duties/requirements/benefits. So we could make a Skills section for nice-to-have. Thus:

            Skills

            with list. 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 Benefits section: from Why Join Us? list. - 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 Pay section: Starting Pay Range: $155k - $172k/yr. We need to output only HTML fragment, no extra text. Let's craft. We'll start with location p. Then

            About the role

            with

            containing The Opportunity paragraphs (maybe two paragraphs). We'll combine. The Opportunity text: "We are seeking a Senior Product Manager to own a core area of the platform end to end—from the problem definition through to the metric that proves it worked. This is not a backlog-grooming role. Our users are dealers, upfitters, OEMs, and fleet buyers whose workflows are genuinely complicated, whose data is genuinely messy, and who will tell you plainly when software wastes their time. You will spend real hours in the field understanding how a fleet buyer thinks about their truck, how it gets specced, upfitted, listed, and sold, then translate that into products that make the whole supply chain faster. You will also help define how AI shows up in our products—which parts of these workflows should be automated, augmented, or left alone—and hold the line on quality when the easy answer is to ship a demo. You'll work closely with design, engineering, and data to sequence work that compounds, and you'll be measured on outcomes for the business and our customers rather than on shipping velocity." We'll put that in a

            . Then Responsibilities list. Responsibilities bullet points: - 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 list: we need to include all points as

          • . Might be long but okay. We'll group maybe but still each as
          • . Could also use nested lists? Not required. We'll just flat list. Let's compile all qualification bullets: - 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 un