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<p>Product Owner, Growth (AI-First)</p>
<p>The Role<br>Belong's growth constraint is supply. Every homeowner who activates on the platform adds a<br>home to the network, creates a resident opportunity, and moves Belong closer to the profitability<br>inflection that defines the next chapter of the company. The homeowner funnel, from first<br>impression through signed agreement and activated listing, is the highest-leverage product<br>surface in the business.<br>Most growth product roles are about optimizing what already exists: faster page loads, shorter<br>forms, better copy. This role is about building something structurally different. Belong's<br>homeowner acquisition funnel is being rebuilt as an AI-native system: conversational intake<br>powered by LLMs, personalized onboarding that adapts dynamically to each homeowner's<br>financial profile, predictive scoring that routes the right lead to the right moment in the Advisor<br>workflow, and agentic follow-up that replaces manual sequences with intelligent, context-aware<br>outreach. The target is a funnel that learns, where every interaction generates signal that makes<br>the next interaction more likely to convert.<br>As Product Owner, Growth, you are the person building that system. You own the homeowner<br>acquisition and activation funnel end to end, from first contact to listed home. You ship in weekly<br>cycles, instrument everything, and hold conversion and experience quality simultaneously. This<br>role is for someone who understands that growth at Belong is not a marketing problem. It is a<br>product problem, and the product is increasingly an AI system.</p>
<p>Examples of What You'll Own<br>The AI-native intake and qualification layer.<br>The first interaction a homeowner has with Belong, whether via belonghome.com, a paid<br>channel, or a referral, is where trust is either established or lost. You will build conversational<br>intake flows powered by LLMs that qualify, capture, and begin converting leads in real time.<br>These are not chatbots with decision trees. They are context-aware systems that understand<br>the difference between a cashflow-positive homeowner who wants yield optimization and a<br>cashflow-negative homeowner who needs a path to profitability, and adapt the conversation, the<br>framing, and the call-to-action accordingly. The Cashflow Lens is not a marketing concept. It is a<br>segmentation variable that must be detected early and carried through every subsequent<br>product interaction.<br>Personalized onboarding and trust architecture.<br>A homeowner considering Belong is anxious. They are considering handing over their most<br>valuable asset to a platform they found online. Conversion at this stage is not a UX problem. It<br>is a trust architecture problem. You will design onboarding sequences that adapt dynamically<br>based on homeowner attributes: property type, cashflow profile, prior rental history, risk signals,</p>
<p>and behavioral signals from in-session activity. You will use LLMs to generate personalized<br>content, market analyses, improvement ROI estimates, comparable listings, that makes the<br>value proposition concrete and specific to their home, not generic.<br>Predictive lead scoring and Advisor routing.<br>Belong's Advisors are the trust-critical human touchpoint in the homeowner funnel. Their time is<br>finite and high-value. You will build the predictive infrastructure that scores every lead on<br>conversion likelihood, property quality, and fit with Belong's ICP, and routes leads to Advisors<br>with the context they need to have the right conversation immediately. You will work with data<br>science to train and evaluate these models, with RevOps to deploy them into the Salesforce<br>workflow, and with Sales leadership to validate signal quality against actual close rates.<br>Agentic follow-up and nurture sequences.<br>Most leads do not convert on the first contact. Today, nurture is a sequence of templated emails.<br>The target state is an AI agent that monitors lead behavior, page views, document opens, return<br>visits, session signals, and generates contextually appropriate, personalized outreach at the<br>right moment, with the right frame, without a human initiating every touchpoint. You will define<br>the agent's decision logic, build the context retrieval pipeline, instrument the output quality, and<br>iterate on conversion impact week over week.<br>Funnel instrumentation and the learning loop.<br>An AI-native funnel without rigorous instrumentation is a black box. You will build the<br>measurement architecture that makes every conversion decision traceable: which intake flow<br>variant produced the lead, which scoring model routed it, which agent-generated touchpoint<br>influenced the next action, which Advisor framing closed it. You will design the feedback loops<br>that push conversion signal back into model evaluation, prompt improvement, and scoring<br>recalibration. The funnel gets smarter every week or it is not an AI-native funnel.<br>The activation gap: agreement to listed home.<br>Signing the agreement is not growth. A listed home is growth. The conversion from signed<br>agreement to activated listing is a product problem with high leverage: homeowners who do not<br>complete inspection scheduling, who abandon the improvement process, or who sit in the<br>pipeline without a live listing represent real lost revenue. You will own the product layer that<br>closes this gap, including AI-assisted improvement planning, proactive homeowner<br>communication anchored to their cashflow profile, and predictive identification of homeowners at<br>risk of churning before listing.</p>
<p>The AI Stack You Will Work With<br>β’ LLM-powered conversational intake with real-time lead qualification and cashflow profile<br>detection<br>β’ Personalized content generation using property-level market data, comparable listings,<br>and improvement ROI modeling<br>β’ Predictive lead scoring models trained on conversion, property quality, and ICP signals<br>β’ Agentic follow-up workflows with behavioral trigger logic and context-aware generation</p>
<p>β’ Retrieval-augmented generation for Advisor preparation: the right context, surfaced at<br>the right moment before the call<br>β’ A/B testing infrastructure applied to AI-generated content variants, not just static copy<br>You will define what this system does. Engineering and data science will build it with you.</p>
<p>What Success Looks Like<br>90 days: The funnel is fully instrumented from first click to activated listing with conversion rates<br>and drop-off points visible at each stage. An AI-assisted intake flow is in production and being<br>tested against the baseline.<br>6 months: Lead-to-listing conversion is measurably above baseline. AI is integrated at a<br>minimum of 3 funnel touchpoints with documented conversion impact per touchpoint. Advisor<br>routing is scored, and the correlation between score and close rate is being tracked.<br>Year 1: The majority of homeowner outreach between first contact and agreement signing is<br>AI-generated, with human Advisors focusing exclusively on trust-critical call moments. CAC on<br>the supply side is trending down. Time-to-activation is compressing quarter over quarter.</p>
<p>Example KPIs You Will Be Held To<br>β’ Lead-to-listing conversion rate (the primary number)<br>β’ Cost per activated listing<br>β’ Time from first contact to listing live<br>β’ AI-assisted funnel touchpoint conversion impact, measured per touchpoint<br>β’ Advisor routing accuracy: scored lead close rate vs. unscored baseline<br>β’ Experiment velocity: instrumented tests shipped per month<br>β’ Homeowner CSAT at onboarding and inspection phases (the constraint: conversion<br>gains cannot come at experience cost)</p>
<p>Who You Are<br>AI systems builder, not AI enthusiast. You have shipped LLM-powered product features in<br>production. You understand prompt engineering, retrieval quality, latency tradeoffs, output<br>evaluation, and model feedback loops. You think about AI systems the way a statistician thinks<br>about models: with explicit assumptions, known failure modes, and a measurement plan built<br>before launch.<br>Growth obsessed, experience constrained. You understand that conversion rate without CSAT<br>is a local maximum. Belong's homeowner retention is a direct function of the expectations set</p>
<p>during acquisition. You optimize the funnel with that downstream constraint live in your head at<br>all times.<br>Quantitatively rigorous. You build experiments with proper hypothesis structures. You distinguish<br>between statistical and practical significance. You know when a metric is being gamed. You<br>make decisions with incomplete data and document the reasoning explicitly.<br>Trust architect. You understand the psychology of a homeowner considering Belong. Anxiety,<br>loss aversion, and the weight of asset delegation are the forces working against conversion. You<br>design for those forces, not around them.<br>Relentlessly shipping. You do not disappear for a quarter. You have something in production<br>every two weeks. You measure it. You decide what to do next. You ship again.</p>
<p>What You Bring<br>β’ 3 to 5 years of product experience with direct ownership over growth funnels in a B2C or<br>marketplace company<br>β’ Demonstrated track record of driving CAC down and conversion up through product<br>decisions, not marketing spend<br>β’ Hands-on experience shipping AI-powered product features, specifically LLM<br>integrations, conversational flows, or agentic workflows, in a production environment with<br>measurable conversion impact<br>β’ Proficiency with experimentation frameworks, funnel analytics, and causal inference in<br>messy real-world data<br>β’ Experience building or evaluating predictive scoring models in a growth context is a<br>strong advantage<br>β’ Prior work in residential real estate, fintech, or marketplace supply-side growth is a<br>meaningful plus</p>
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