Lead Product Manager, AI Strategy
for section headings. Use - for any list of duties/requirements/benefits. Use
for prose.
We must not repeat job title as heading. So no
Lead Product Manager
.
We must not put Job ID/requisition numbers as headings.
We must output only HTML fragment, no /, no markdown fences.
Let's craft.
First, About the role: combine intro paragraphs up to before "What You'll Do:" maybe.
Let's extract:
"Recognized as the No. 1 site trusted by real estate professionals, Realtor.com® has been at the forefront of online real estate for over 25 years, connecting buyers, sellers, and renters with trusted insights and expert guidance to find their perfect home. Through its robust suite of tools, Realtor.com® not only makes a significant impact on the real estate industry at large, but for consumers, navigating the biggest purchase they will make in their life, by providing a user experience that is easy to use, easy to understand, and most of all, easy to make decisions.Join us on our mission to empower more people to find their way home by breaking barriers to entry, making the right connections, and building confidence through expert guidance. We're seeking a Lead Product Manager to define the vision and strategy for RDC's evolving AI-native product experiences, transforming how clients interact with our products and bringing our data and insights directly into how they run their business. This role sits on the Core Product Strategy team and spans the everyday products our clients use, the internal tools that help our own people serve them, and scalable data products and backend integrations that give our largest enterprise segments direct access to RDC's data and insights, integrated into their own brokerage ecosystems.You'll help define the strategy behind where AI creates real value for RDC, not just where it's technically possible, and you'll work directly with executive stakeholders across the business, including GMs and the Strategy team, to align that point of view with the broader portfolio and secure the investment to build it.This role goes deeper than identifying features AI could enable. You need a working understanding of the data and technical foundations, model selection, data quality and pipelines, evaluation, cost, and latency, that determine whether an AI or data capability can actually scale in production, and you'll partner closely with engineering and data science to make sure it can, and with the Product Experience Delivery team to make sure it delivers on adoption and business results."
That is a big paragraph; we can keep as .
Now Responsibilities: "What You'll Do:" list items.
List:
- Define the vision for how clients interact with RDC's products in an AI-native model, reimagining traditional workflows around conversational, proactive, and insight-driven experiences rather than bolting AI onto existing screens.
- Own the product strategy and roadmap for an AI-native interaction model across the Client portfolio, spanning client-facing products, internal tools that help our teams better serve clients, and data products that package RDC's data and insights for external use.
- Translate broader Core Product Strategy priorities into concrete, sequenced AI product bets, in partnership with the Advanced Insights sub-team and other Client Product areas.
- Define the north star for AI-native capabilities and partner with data science and engineering to ensure the underlying data and technical strategies and roadmaps support that foundational need, covering model selection and tradeoffs, data quality and availability, pipeline and infrastructure requirements, and what it takes to scale a capability from prototype to production.
- Define the data requirements that underpin new AI capabilities, including what data needs to be captured, cleaned, or integrated, and work with data teams to close gaps.
- Set and defend quality, accuracy, and trust standards for client-facing AI outputs, understanding where model limitations create real risk to clients and the business.
- Build the business case for new AI capabilities, including expected client and business impact, technical cost and complexity, and buy vs. build vs. partner tradeoffs.
- Design internal AI tools that make Client Product, Sales, and Support teams more effective at serving clients, from insight generation to workflow automation.
- Define and grow RDC's data product strategy, identifying which data and insights can be packaged and sold to clients and partners, and what it takes technically and commercially to deliver them responsibly and at scale.
- Design scalable backend integrations that give large enterprise segments direct access to RDC's data and insights, integrated into their own brokerage ecosystems.
- Partner with enterprise and partnerships stakeholders to shape the data access and integration strategy for our largest accounts, balancing client value against data governance and commercial considerations.
- Stay ahead of the external AI landscape, competitor capabilities, and emerging techniques, and translate what matters into RDC's product strategy.
- Partner directly with executive stakeholders across the business, including GMs and the Strategy team, to define where AI creates real value for RDC and to align that point of view with the broader portfolio strategy.
- Partner with legal, privacy, and trust and safety functions to navigate the data and model risks specific to AI products.
- Represent AI product strategy in planning and review forums, communicating tradeoffs and progress clearly to both technical and non-technical audiences.
Now Requirements: maybe Education & Experience section.
Education & Experience list:
- Bachelor's degree in a technical field or equivalent experience; a background in data science, engineering, or applied AI is a strong plus.
- 10+ years of product management experience, including meaningful, recent experience shipping AI or ML-powered features in a client-facing product.
- Demonstrated experience partnering directly with data science and engineering teams on model selection, evaluation, and scaling decisions.
- Experience building both client-facing and internal-facing tools is a plus.
- Experience with enterprise or B2B data integrations, APIs, data-sharing agreements, or partner ecosystems is a plus.
- Experience in real estate, marketplace, or subscription/services businesses is a plus but not required.
We could treat this as Requirements or Qualifications. The instruction: Use headings for About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to decide which content goes where.
Typically: Requirements = mandatory qualifications; Qualifications = preferred; Skills = specific skills. But we can map as we see fit.
We have Education & Experience (some mandatory, some plus). We also have "What You'll Bring" list which includes skills and qualifications.
Let's extract "What You'll Bring" list:
- Deep hands-on experience building and shipping AI-native, client-facing products, not just experimenting with AI internally.
- A track record of designing new interaction models, conversational, proactive, or insight-driven experiences, rather than only adding AI features to existing workflows.
- Working technical fluency in how AI products are actually built: model types and tradeoffs, data pipelines, evaluation and monitoring, and the infrastructure choices that affect cost and scale.
- Comfort partnering closely with engineering and data science as a peer in technical conversations, not just a translator of requirements.
- An understanding that goes beyond what a model can do, to what it takes operationally and technically to serve that capability reliably to real clients at scale.
- Strong product judgment on where AI creates real client value versus where it's a novelty, and the discipline to say no to the latter.
- Experience defining or scaling data products, including packaging data and insights into offerings that can be sold externally, is a strong plus.
- Comfort working with enterprise or B2B stakeholders on data access, integration, and partnership strategy for large accounts.
- Experience defining and enforcing quality and trust standards for AI outputs in a client-facing product.
- Strong cross-functional collaboration skills, able to align data science, engineering, design, legal, and business stakeholders around a single roadmap.
- Clear written and verbal communication, able to explain technical AI tradeoffs to non-technical stakeholders and vice versa.
These are skills/qualifications.
Thus we can assign:
- Requirements: Education & Experience (maybe the mandatory ones: Bachelor's degree, 10+ years PM experience, demonstrated experience partnering with data science/engineering). But we have pluses. To avoid inventing, we can just put the whole Education & Experience list under Requirements.
- Qualifications: What You'll Bring list (skills). Or we could split: Qualifications = What You'll Bring, Skills = maybe same? But we need to avoid thin sections. We could combine Qualifications and Skills into one section? But instruction says use headings only when posting has real specific content for it. We have content for both Qualifications and Skills; but we could treat Qualifications as the What You'll Bring list, and Skills maybe empty? Better to not create empty sections. So we could put What You'll Bring under Qualifications, and maybe Skills under something else? But we have no separate skills list beyond that. Could treat Qualifications as the list, and Skills omitted. Or we could put What You'll Bring under Skills, and Qualifications under Education & Experience. Let's decide.
We need to include sections only when posting has real specific content. So we need to decide which headings we will output: About the role, Responsibilities, Requirements, Qualifications, Benefits, Schedule. Possibly also Skills if we have content.
We have content for Skills: the What You'll Bring list includes skills like technical fluency, product judgment, cross-functional collaboration, communication. So we could output Skills section with that list.
Qualifications could be Education & Experience (degree, years experience). That seems appropriate.
Thus:
- About the role: intro paragraphs.
- Responsibilities: What You'll Do list.
- Requirements: Education & Experience list (maybe treat as mandatory).
- Qualifications: What You'll Bring list.
- Benefits: How We Reward You list.
- Schedule: How We Work paragraph (including in-person collaboration days).
- Pay: omitted (no content).
- Skills: maybe we could omit if we used Qualifications for skills. But we already used Qualifications for What You'll Bring (which is skills). So we could rename Qualifications
for prose. We must not repeat job title as heading. So no
Lead Product Manager
. We must not put Job ID/requisition numbers as headings. We must output only HTML fragment, no /, no markdown fences. Let's craft. First, About the role: combine intro paragraphs up to before "What You'll Do:" maybe. Let's extract: "Recognized as the No. 1 site trusted by real estate professionals, Realtor.com® has been at the forefront of online real estate for over 25 years, connecting buyers, sellers, and renters with trusted insights and expert guidance to find their perfect home. Through its robust suite of tools, Realtor.com® not only makes a significant impact on the real estate industry at large, but for consumers, navigating the biggest purchase they will make in their life, by providing a user experience that is easy to use, easy to understand, and most of all, easy to make decisions.Join us on our mission to empower more people to find their way home by breaking barriers to entry, making the right connections, and building confidence through expert guidance. We're seeking a Lead Product Manager to define the vision and strategy for RDC's evolving AI-native product experiences, transforming how clients interact with our products and bringing our data and insights directly into how they run their business. This role sits on the Core Product Strategy team and spans the everyday products our clients use, the internal tools that help our own people serve them, and scalable data products and backend integrations that give our largest enterprise segments direct access to RDC's data and insights, integrated into their own brokerage ecosystems.You'll help define the strategy behind where AI creates real value for RDC, not just where it's technically possible, and you'll work directly with executive stakeholders across the business, including GMs and the Strategy team, to align that point of view with the broader portfolio and secure the investment to build it.This role goes deeper than identifying features AI could enable. You need a working understanding of the data and technical foundations, model selection, data quality and pipelines, evaluation, cost, and latency, that determine whether an AI or data capability can actually scale in production, and you'll partner closely with engineering and data science to make sure it can, and with the Product Experience Delivery team to make sure it delivers on adoption and business results." That is a big paragraph; we can keep as. Now Responsibilities: "What You'll Do:" list items. List: - Define the vision for how clients interact with RDC's products in an AI-native model, reimagining traditional workflows around conversational, proactive, and insight-driven experiences rather than bolting AI onto existing screens. - Own the product strategy and roadmap for an AI-native interaction model across the Client portfolio, spanning client-facing products, internal tools that help our teams better serve clients, and data products that package RDC's data and insights for external use. - Translate broader Core Product Strategy priorities into concrete, sequenced AI product bets, in partnership with the Advanced Insights sub-team and other Client Product areas. - Define the north star for AI-native capabilities and partner with data science and engineering to ensure the underlying data and technical strategies and roadmaps support that foundational need, covering model selection and tradeoffs, data quality and availability, pipeline and infrastructure requirements, and what it takes to scale a capability from prototype to production. - Define the data requirements that underpin new AI capabilities, including what data needs to be captured, cleaned, or integrated, and work with data teams to close gaps. - Set and defend quality, accuracy, and trust standards for client-facing AI outputs, understanding where model limitations create real risk to clients and the business. - Build the business case for new AI capabilities, including expected client and business impact, technical cost and complexity, and buy vs. build vs. partner tradeoffs. - Design internal AI tools that make Client Product, Sales, and Support teams more effective at serving clients, from insight generation to workflow automation. - Define and grow RDC's data product strategy, identifying which data and insights can be packaged and sold to clients and partners, and what it takes technically and commercially to deliver them responsibly and at scale. - Design scalable backend integrations that give large enterprise segments direct access to RDC's data and insights, integrated into their own brokerage ecosystems. - Partner with enterprise and partnerships stakeholders to shape the data access and integration strategy for our largest accounts, balancing client value against data governance and commercial considerations. - Stay ahead of the external AI landscape, competitor capabilities, and emerging techniques, and translate what matters into RDC's product strategy. - Partner directly with executive stakeholders across the business, including GMs and the Strategy team, to define where AI creates real value for RDC and to align that point of view with the broader portfolio strategy. - Partner with legal, privacy, and trust and safety functions to navigate the data and model risks specific to AI products. - Represent AI product strategy in planning and review forums, communicating tradeoffs and progress clearly to both technical and non-technical audiences. Now Requirements: maybe Education & Experience section. Education & Experience list: - Bachelor's degree in a technical field or equivalent experience; a background in data science, engineering, or applied AI is a strong plus. - 10+ years of product management experience, including meaningful, recent experience shipping AI or ML-powered features in a client-facing product. - Demonstrated experience partnering directly with data science and engineering teams on model selection, evaluation, and scaling decisions. - Experience building both client-facing and internal-facing tools is a plus. - Experience with enterprise or B2B data integrations, APIs, data-sharing agreements, or partner ecosystems is a plus. - Experience in real estate, marketplace, or subscription/services businesses is a plus but not required. We could treat this as Requirements or Qualifications. The instruction: Use headings for About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to decide which content goes where. Typically: Requirements = mandatory qualifications; Qualifications = preferred; Skills = specific skills. But we can map as we see fit. We have Education & Experience (some mandatory, some plus). We also have "What You'll Bring" list which includes skills and qualifications. Let's extract "What You'll Bring" list: - Deep hands-on experience building and shipping AI-native, client-facing products, not just experimenting with AI internally. - A track record of designing new interaction models, conversational, proactive, or insight-driven experiences, rather than only adding AI features to existing workflows. - Working technical fluency in how AI products are actually built: model types and tradeoffs, data pipelines, evaluation and monitoring, and the infrastructure choices that affect cost and scale. - Comfort partnering closely with engineering and data science as a peer in technical conversations, not just a translator of requirements. - An understanding that goes beyond what a model can do, to what it takes operationally and technically to serve that capability reliably to real clients at scale. - Strong product judgment on where AI creates real client value versus where it's a novelty, and the discipline to say no to the latter. - Experience defining or scaling data products, including packaging data and insights into offerings that can be sold externally, is a strong plus. - Comfort working with enterprise or B2B stakeholders on data access, integration, and partnership strategy for large accounts. - Experience defining and enforcing quality and trust standards for AI outputs in a client-facing product. - Strong cross-functional collaboration skills, able to align data science, engineering, design, legal, and business stakeholders around a single roadmap. - Clear written and verbal communication, able to explain technical AI tradeoffs to non-technical stakeholders and vice versa. These are skills/qualifications. Thus we can assign: - Requirements: Education & Experience (maybe the mandatory ones: Bachelor's degree, 10+ years PM experience, demonstrated experience partnering with data science/engineering). But we have pluses. To avoid inventing, we can just put the whole Education & Experience list under Requirements. - Qualifications: What You'll Bring list (skills). Or we could split: Qualifications = What You'll Bring, Skills = maybe same? But we need to avoid thin sections. We could combine Qualifications and Skills into one section? But instruction says use headings only when posting has real specific content for it. We have content for both Qualifications and Skills; but we could treat Qualifications as the What You'll Bring list, and Skills maybe empty? Better to not create empty sections. So we could put What You'll Bring under Qualifications, and maybe Skills under something else? But we have no separate skills list beyond that. Could treat Qualifications as the list, and Skills omitted. Or we could put What You'll Bring under Skills, and Qualifications under Education & Experience. Let's decide. We need to include sections only when posting has real specific content. So we need to decide which headings we will output: About the role, Responsibilities, Requirements, Qualifications, Benefits, Schedule. Possibly also Skills if we have content. We have content for Skills: the What You'll Bring list includes skills like technical fluency, product judgment, cross-functional collaboration, communication. So we could output Skills section with that list. Qualifications could be Education & Experience (degree, years experience). That seems appropriate. Thus: - About the role: intro paragraphs. - Responsibilities: What You'll Do list. - Requirements: Education & Experience list (maybe treat as mandatory). - Qualifications: What You'll Bring list. - Benefits: How We Reward You list. - Schedule: How We Work paragraph (including in-person collaboration days). - Pay: omitted (no content). - Skills: maybe we could omit if we used Qualifications for skills. But we already used Qualifications for What You'll Bring (which is skills). So we could rename Qualifications