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Rayda is seeking a seasoned product leader to shape the strategy and execution of a remote, SaaS-focused product portfolio. You will own discovery, roadmaps, and success metrics while collaborating with Engineering, Design, Sales, Success, and Operations to drive measurable business outcomes.
The role emphasizes AI-enabled development, structured QA and release processes, and data-driven decision making across globally distributed teams.
Rayda is an asset lifecycle management company, enabling businesses to acquire, manage, track and dispose of their fixed assets globally.
This is a remote position.
Develop and execute a product strategy aligned with Rayda's business objectives.
Own product discovery, customer research, competitor analysis, and roadmap prioritisation.
Define product objectives, success metrics, and measurable business outcomes.
Identify opportunities to improve existing products and introduce new capabilities.
Collaborate with Engineering, Design, Sales, Customer Success, Operations, Finance, and Compliance.
Gather, evaluate, and prioritise product requirements based on customer needs and business value.
Communicate roadmap decisions, timelines, dependencies, and trade-offs.
Establish structured feedback and engagement processes across teams.
Translate business requirements into clear product requirement documents (PRDs), user stories, and acceptance criteria.
Own backlog prioritisation, sprint planning coordination, and product delivery tracking.
Work closely with Engineering to manage technical dependencies and development priorities.
Ensure product documentation is accurate and accessible.
Establish effective product testing, bug tracking, and release management processes.
Coordinate QA activities and ensure features meet defined acceptance criteria.
Improve release readiness, product quality, and delivery predictability.
Establish clear product sign-off and escalation procedures.
Implement and optimise product analytics tools, including PostHog or similar platforms.
Monitor feature adoption, user engagement, product utilisation, and customer journeys.
Develop dashboards and performance reports to support data-driven decisions.
Use product insights and customer feedback to drive continuous improvement.
Leverage AI tools to accelerate product research, documentation, prototyping, and experimentation.
Build functional prototypes and micro-products using AI-assisted development tools.
Validate new product ideas through rapid experimentation.
Identify opportunities to integrate AI capabilities into Rayda's products and internal workflows.
Establish scalable product management processes and operating standards.
Drive alignment between Product, Engineering, Design, and business teams.
Promote accountability, effective communication, and continuous improvement.
Identify process and capability gaps as the product organisation scales.
Minimum of 7 years of product management experience , including at least 3 years in a senior product management role or equivalent.
Proven experience managing the end-to-end product lifecycle in a SaaS or technology environment.
Demonstrated ability to develop product strategies, manage roadmaps, and prioritise competing business requirements.
Strong experience collaborating with Engineering, Design, QA, and cross-functional stakeholders.
Experience establishing product development, QA, bug resolution, and release management processes.
Hands-on experience with product analytics tools such as PostHog, Mixpanel, or Amplitude.
Practical experience using AI tools for product development, prototyping, and workflow automation.
Strong analytical, communication, stakeholder management, and leadership skills.
Good understanding of APIs, webhooks, third-party integrations, SaaS architecture, databases, and technical dependencies.
Experience with B2B SaaS, enterprise software, or IT operations products is an advantage.
Within the first six months, the successful candidate will have:
Established a clear and prioritised product roadmap.
Implemented structured product discovery and requirements management processes.
Improved sprint planning, delivery visibility, and release management.
Introduced effective QA coordination and bug resolution frameworks.
Established actionable product analytics and performance dashboards.
Demonstrated practical use of AI in product experimentation.
Improved collaboration, accountability, and product delivery predictability.