Senior AI Product Manager

Accountabilities:: Lead the full product development lifecycle, from discovery workshops and data mapping through platform development, reporting views, user training, and final handover. Own delivery plans, milestones, acceptance gates, project scope, and client commitments across fixed-fee engagements with defined deadlines. Establish gate criteria, supporting evidence, and acceptance requirements with clients before project sign-off; manage scope changes and clearly identify work for future delivery cycles. Define value baselines with client finance teams and track measurable outcomes throughout each engagement. Prioritize reliable data foundations by coordinating source mapping, data quality rules, exception routing, data lineage, and locked data snapshots before reporting views are developed. Facilitate workshops with asset management, finance, investor relations, deal teams, and technology stakeholders to define reporting needs, KPIs, view specifications, and acceptance criteria. Partner with engineering and data science teams to identify appropriate AI use cases, including agent-based data mapping, document extraction, retrieval with cited sources, and automated commentary drafting. Ensure financial reporting remains based on fixed, auditable calculations approved by the client. Serve as the primary day-to-day contact for executive sponsors, business owners, and technology leads, communicating progress clearly and escalating risks early. Lead incumbent platform retirement activities, including historical data migration, parallel runs, sign-off, and transition planning. Coordinate role-based user training and ensure complete documentation and operational knowledge transfer to the client's platform owner. Requirements: At least 5 years of product management experience, with hands-on ownership of every phase of the product development lifecycle and a track record of delivering custom software for external clients. At least 2 years of experience within or directly serving the institutional real estate sector, such as an owner-operator, REIT, private equity real estate firm, fund manager, or real estate technology or data vendor. Strong understanding of real estate financial and operational concepts, including rent rolls, operating statements, budgets versus actuals, T-12 statements, loan abstracts, JV waterfall summaries, WALT, DSCR, NOI, and equity funded versus committed. Hands-on experience with property accounting systems such as Yardi, MRI, or RealPage, plus at least one platform such as Juniper Square, Altrio, VTS, ARGUS, or Pereview. Experience delivering data platform products, such as data warehouses, data lakes, reporting, or BI solutions, where data accuracy and stakeholder trust are central to adoption. Strong understanding of data ingestion, mapping, reconciliation, data quality, exception handling, lineage, and immutable snapshots. Solid technical knowledge of LLM-based extraction, retrieval with cited sources, agent workflows, and human-in-the-loop approval processes, with the ability to translate business needs into actionable product and engineering specifications. Familiarity with the Microsoft data ecosystem, including Azure, Fabric or Synapse, and Power BI, as well as single sign-on, role- and fund-level permissions, audit logging, and test environments. Proven experience managing fixed-fee, milestone-based consulting engagements where payments depend on client acceptance, including defining clear acceptance criteria. At least 5–7 years of experience leading multidisciplinary teams across design, engineering, and data. Strong client-facing presence and confidence working with senior stakeholders such as CFOs, COOs, heads of asset management, and technology leaders. Analytical and data-driven approach, with the ability to define KPIs, investigate discrepancies, understand performance against plans, and make evidence-based decisions. Proficiency with project and collaboration tools such as Google Workspace, ClickUp, Figma, and Jira. Availability to overlap with US Eastern business hours for workshops, milestone reviews, and training. Strongly preferred: Experience retiring or replacing an incumbent platform against a contractual deadline, including managing a parallel run. Familiarity with quarterly investor reporting, fund-level reporting, or due diligence questionnaire (DDQ) response processes. Experience with data governance, including ownership models, stewardship, and business-led approval of data mappings and thresholds. Benefits: Remote work arrangement. Contract employment. Competitive compensation package, including equity and performance-based bonuses. Substantial participation in company profits. Potential growth path to C-level leadership based on performance. All-expenses-paid company retreats in unique locations. Salary range, healthcare coverage, and specific paid leave arrangements are not specified in the job description.