Product Operations Lead

About the Role We are seeking a Product Operations Lead to play a key role in product launches, planning processes, AI adoption and operational efficiency. This role will ensure product availability, process optimization, and KPI tracking, working cross-functionally with Product, Engineering, Marketing, and Customer Success teams. The ideal candidate will drive seamless product delivery, scale best practices, and enhance the product management lifecycle. Key Responsibilities: Product Launch & Go-to-Market Readiness Drive cross-functional product launch planning , ensuring clear responsibilities and execution timelines. Develop together with Product Marketing scalable launch playbooks to standardize and optimize the GTM process. Track and measure launch success through key metrics such as adoption, usage, and retention. Ensure internal teams are enabled with product documentation, updates, release notes and other enablement principles Partner with Customer Success to optimize post-launch support and user experience. Monitor product readiness and ensure alignment between Product, R&D, Support and GTM teams . AI & Builder Approach Work AI-first - use Claude and the connected stack (JIRA, Confluence, Vitally, Gong, Slack) as the default method for drafting, analysis, and synthesis, not an occasional shortcut. Build the tooling you need. Ship lightweight internal tools - scripts, agents, digests, dashboards - that remove recurring manual work in release and launch operations, without waiting for an engineering roadmap slot. Contribute to Product Brain , our internal AI knowledge layer, and design operational workflows that pull context from connected systems rather than reassembling it by hand each cycle. Own AI adoption within Product Operations - find the manual work a system should be doing, prototype the replacement, and prove it works before asking anyone to adopt it. Apply judgment on where automation belongs . Know which decisions, escalations, and communications need a human, and keep those human. KPI Tracking & Operational Excellence Define and track key performance indicators (KPIs) to measure release and launch effectiveness. Identify and implement process improvements to enhance speed and efficiency. Leverage data insights to drive continuous optimization of product operations workflows . Own and optimize tooling and dashboards to provide real-time visibility into product health. Partner with R&D to streamline deployment workflows, product maintenance and incident response guidelines . Qualifications & Experience: Required Skills & Experience At least 7 years in Product Operations, Business Development or Product Lifecycle Management within a SaaS or technology-driven company. Experience with feature or Product delivery , value driven decision making and coordinating product launches and releases . Experience in agile & kanban development, release processes, and change management . Hands-on daily use of AI tools in real work , with concrete examples of what changed as a result. Proficiency in JIRA, Confluence, or similar release tracking tools . Data-driven mindset with experience in tracking and analyzing product performance metrics , overseeing execution strategy and tactical improvements Comfortable contributing to engineering discussions around technology decisions and strategy related to a product pipeline Excellent communication, collaboration, and stakeholder management skills . Desired Skills Knowledge of SaaS business models, pricing strategies, and GTM execution . Familiarity with customer adoption and retention analytics . Experience working with API-first or developer-focused products . Experience building internal tools or automations that other teams actually adopted . Familiarity with LLM-based workflows — prompt and agent design, MCP connectors, or similar. Success Metrics: Efficient and predictable releases , with minimal delays or incidents. Stronger cross-functional coordination , improving launch readiness. Standardized product operations processes , improving efficiency and scalability. Data-driven decision-making , leveraging insights to optimize release and launch performance. Higher product adoption rates , increasing user engagement and retention. Reduced manual operational load , with recurring coordination and reporting work replaced by AI-assisted workflows and internal tooling.