Product Manager, Data Analytics

We need to output HTML fragment with sections: About the role? Actually the posting includes "About Vivmark Residential" but we should not repeat job title as heading. Use headings for sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. However we should only include sections when posting has real specific content. The posting includes: "About Vivmark Residential" (company description) maybe we treat as About the role? Actually "About the role" is a heading we can use for description of role. The posting includes "The Role Reporting to the Senior Manager..." etc. So we can put that under

About the role

with paragraphs. Responsibilities: The "What You'll Do" bullet list. So we can put under

Responsibilities

with

• items. Requirements: The "Requirements" section includes bullet list of qualifications. So

  Requirements

  with list. Qualifications: Might be overlapping with Requirements; but we can maybe combine? The instruction says use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Skills). We need to decide which sections to include based on content. The posting includes "Requirements" and also mentions "Bachelor's degree required; degree in Business, Computer Science, Engineering, Analytics, Economics, or related field preferred." That's qualification. Also "Experience in Revenue Management, pricing optimization, forecasting, yield management, or similar analytical domains". That's also qualification. Could treat as Qualifications. But we also have "Skills" maybe not explicit. There's mention of "Strong technical working knowledge of machine learning models, data pipelines, model deployment workflows, and APIs—with hands-on experience partnering with data scientists and engineers." That's skill. Also "Proficiency with SQL, analytics environments, and BI tools (Power BI, Tableau, etc.)". Also "Proven experience with Agile methodologies, backlog management, sprint execution, and iterative product delivery." Also "Experience defining and managing program-level KPIs, establishing review cadences, and leading data-driven decision processes." Also "Excellent verbal and written communication skills". So we could have a Skills section. Benefits: The posting includes "We Offer: A competitive benefits package that supports your health, financial well-being, and work-life balance, including medical, dental, and vision coverage, a 401(k) with company match, paid vacation and company holidays, and exclusive team member housing discounts." So we can have Benefits section. Pay: The posting includes "Salary $106,228-$132,785 per year; in addition to base salary, a discretionary 16.0% annual bonus is provided." So we can have Pay section. Schedule: The posting includes "PLEASE NOTE: Our current onsite work schedule requires attendance from Monday through Thursday each week, with remote work permitted on Fridays." So we can have Schedule section. We should not include sections like "Qualifications" if we already covered in Requirements? But we can include both if content distinct. However we must avoid filler/placeholder. We have content for each. We need to use

  for section headings. Use

  • for any list of duties/requirements/benefits, and

    for prose. We must not repeat job title as heading. Not include Job ID etc. Remove boilerplate like EEO, legal, etc. Also remove duplicated lines, application instructions, cookie/navigation, etc. Also remove hashtags at end. We need to preserve all substantive information. So we need to include the company description (About Vivmark Residential) maybe under About the role? Or we could have a separate "About Vivmark Residential" but not in allowed headings list. The allowed headings list includes About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we should put company description maybe under About the role? Actually About the role is for role description; but we could also include a brief company overview under About the role or maybe we could add a separate section like "About Vivmark Residential" but not allowed. The instruction: "use

    for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." It says e.g., meaning those are examples, not exclusive list? It says "use

    for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." Likely they expect us to use those headings where appropriate. So we should map content to those headings. Thus: - About the role: includes description of role and maybe company overview? The "About Vivmark Residential" is company description, not role. But we could still put it under About the role as introductory paragraph. Or we could create a separate heading like "About Vivmark Residential" but not in list; risk of being considered not following instruction. Safer to include company description within About the role paragraph. - Responsibilities: the "What You'll Do" list. - Requirements: the bullet list under "Requirements". That includes education, experience, technical knowledge, etc. - Qualifications: maybe we could treat the "Experience in Revenue Management, pricing optimization, forecasting, yield management, or similar analytical domains" as qualification. But we already have that in Requirements. Could combine. However we need to avoid duplication. Perhaps we can split: Requirements for education and experience; Qualifications for specific domain experience and skills? But the instruction says prefer a few substantive sections over many thin ones. So maybe we combine Requirements and Qualifications into one section? But they ask to use headings for those sections; we can include both if we have distinct content. However we risk thin sections. Let's see content: Requirements bullet list includes: - Bachelor's degree required; degree in Business, Computer Science, Engineering, Analytics, Economics, or related field preferred. - 5 plus years of experience in Product Management, Technical Program Management, or Engineering, with a focus on data, analytics, or platform development. - Strong technical working knowledge of machine learning models, data pipelines, model deployment workflows, and APIs—with hands-on experience partnering with data scientists and engineers. - Demonstrated ability to integrate machine learning driven features into end-to-end production systems and evaluate model performance. - Proficiency with SQL, analytics environments, and BI tools (Power BI, Tableau, etc.). - Proven experience with Agile methodologies, backlog management, sprint execution, and iterative product delivery. - Experience defining and managing program-level KPIs, establishing review cadences, and leading data-driven decision processes. - Experience in Revenue Management, pricing optimization, forecasting, yield management, or similar analytical domains (real estate, hospitality, travel, etc.). - Excellent verbal and written communication skills with ability to translate between technical and non-technical stakeholders. That's a comprehensive list. Could be considered Requirements. Qualifications maybe not needed separately. Skills: Could extract specific skills like "machine learning models, data pipelines, APIs, SQL, BI tools, Agile, KPI management, communication". But that overlaps. Given instruction to prefer few substantive sections, we could have: - About the role (includes company overview and role description) - Responsibilities (What You'll Do) - Requirements (includes education, experience, technical skills, etc.) - Benefits - Pay - Schedule We could omit Qualifications and Skills as separate sections to avoid thin sections. Check if we have any content for Skills separate from Requirements: Not really distinct; we can embed skills within Requirements. Thus output HTML with those sections. We must not include any markdown fences. Just raw HTML. Let's craft:

    About the role

    Vivmark Residential exists to set the mark for what home can be. Formed through the merger of AvalonBay Communities and Equity Residential, Vivmark brings together two industry leaders with a shared commitment to excellence, innovation, and delivering exceptional living experiences. Our vision is to be the most trusted and best-performing rental housing company in America - one that continues to get better as it grows. And we're looking for people who want to help us do it. As an owner, developer, and manager of more than 180,000 apartment homes in major markets across the US, we have the scale to offer something most companies can't. At Vivmark, team members move up and across functions, markets, and disciplines, building lasting careers. We invest in team members' growth, development, and capabilities that make them and us better, and we hold ourselves to the highest standard as an employer. The best people want to work here. We hope you're one of them.

    Reporting to the Senior Manager, Data Analytics, the candidate will be responsible for defining, delivering and continuously enhancing Vivmark Residential's analytics and machine-learning enabled products. This role sits at the intersection of data science, software engineering, and business strategy. Translating analytical insights into scalable systems and partnering with technical teams to operationalize machine learning models into production-grade applications. The ideal candidate brings a blend of product management discipline, technical fluency in machine learning and data pipelines, and strong program management skills. This role collaborates closely with the Senior Software Engineer, data scientists, and business partners to build intuitive, reliable products that drive measurable impact on pricing, forecasting, and operational decision-making.

    Responsibilities

    • Collaborates with business, analytics, and engineering teams to define the vision, strategy, and long-term roadmap for data and machine learning products.
    • Translates complex pricing, forecasting, and operational needs into clear product requirements, user stories, and acceptance criteria.
    • Leads prioritization across competing business and technical needs, managing backlogs and release plans.
    • Serves as product spokesperson and subject matter expert for analytics capabilities across the enterprise.
    • Works directly with data scientists to understand and refine model outputs, feature relevance, performance metrics, and constraints.
    • Applies strong technical knowledge of machine learning workflows, pipelines, APIs, and model monitoring to guide product direction and evaluate trade-offs.
    • Partners with the Senior Software Engineer Data & Analytics Products to operationalize models into user-facing applications and enterprise systems.
    • Ensures all ML-driven product features emphasize explain ability, compliance, and reliability.
    • Defines and tracks key KPIs (e.g., model accuracy, revenue lift, adoption, system reliability) to measure product performance and business impact.
    • Establishes a structured cadence for reviewing KPIs with cross-functional partners and initiating corrective actions or enhancements.
    • Drives successful delivery of milestones across pilots, MVP releases, and product iterations.
    • Leads retrospectives and drives continuous improvement of analytics workflows, model governance, and product execution processes.
    • Acts as the bridge between data scientists, software engineers, IT, Pricing, and Operations teams.
    • Facilitates workshops, demos, feedback sessions, and cross-functional updates to maintain alignment and transparency.
    • Communicates complex analytical concepts in a clear, business-relevant manner for stakeholders at all levels.
    • Supports change management, training, and adoption for new analytics capabilities across the organization.

    Requirements

    • Bachelor's degree required; degree in Business, Computer Science, Engineering, Analytics, Economics, or related field preferred.
    • 5 plus years of experience in Product Management, Technical Program Management, or Engineering, with a focus on data, analytics, or platform development.
    • Strong technical working knowledge of machine learning models, data pipelines, model deployment workflows, and APIs—with hands-on experience partnering with data scientists and engineers.
    • Demonstrated ability to integrate machine learning driven features into end-to-end production systems and evaluate model performance.
    • Proficiency with SQL, analytics environments, and BI tools (Power BI, Tableau, etc.).