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Closed on July 9, 2026.
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Product Manager – Maintenance Platform
Cmms (maintenance Management System)
Customer Experience
Partner Management
Product Management
Product Manager
Product Operations
Project Management
Vendor Management
Workflow Management
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Job Description
TMS LLC is seeking a Product Manager to lead the development and scaling of the Maintenance Platform across communities, guiding strategy, execution, and cross-functional alignment to improve maintenance operations, resident experience, and operational efficiency.
Responsibilities
- Define and execute the product roadmap for the maintenance platform, including work order management, inspections, preventive maintenance, and resident communication.
- Collaborate with Operations, Engineering, and external vendors to translate business needs into scalable product solutions.
- Own end-to-end delivery from requirements and prioritization through launch and iteration.
- Streamline workflows across scheduling, technician productivity, and automated resident engagement.
- Lead the integration strategy to ensure seamless data flow and interoperability with systems such as PMS, CRM, and middleware.
- Leverage data and user feedback to continuously improve product performance and adoption.
- Support pilot programs and scale successful solutions across the portfolio.
Requirements
- 5+ years of product management experience, preferably in B2B SaaS, operations, or platform products.
- Experience building workflow-based products such as work order systems, CRM, or operations platforms.
- Familiarity with AI/ML-driven products or automation tools; hands-on experience is not required, but strong product sense is expected.
- Strong stakeholder management and cross-functional leadership skills.
- Analytical mindset with experience using data to inform product decisions.
Technologies
- PMS
- CRM
- Middleware
AI / DATA EXPECTATIONS
- Identify opportunities to leverage AI to automate workflows, including work order intake, routing, and communication.
- Partner with engineering on AI-enabled features such as recommendation engines, automation, and workflow optimization.
- Define success metrics and evaluate AI performance (accuracy, efficiency, user adoption).
- Ensure responsible AI usage, including data quality, transparency, and operational reliability.
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