Samsara is the pioneer of the Connected Operations™ Cloud, enabling organizations to harness IoT data for improved operations. The Sr. Support Tools Product Manager will drive strategy and influence the long-term direction of the sales technology stack, focusing on enhancing the core support tools to deliver substantial impact.
Responsibilities:
- Fully own the success of your tool(s) within Samsara’s revenue stack. This includes:
- Discovery: Lead deep user research to identify, document, and anticipate core customer support pain points and future GTM tooling needs
- Design: Draft comprehensive Product Requirements Documents (PRDs) and design artifacts (wireframes), ensuring alignment with business goals and automated workflows
- Build: Oversee feature execution and drive rigorous usability testing, collaborating with GTMS, Sales Ops, and AI + Data Analytics teams
- Launch: Coordinate all aspects of release, including change management, enablement programs, and documentation for seamless field adoption
- Adopt & Iterate: Define and track key performance indicators (KPIs and adoption metrics) to measure business impact and inform rapid roadmap iterations
- Champion, role model, and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices
Requirements:
- 5-8 years of Customer Support tooling experience in a fast-paced SaaS environment
- Bachelor's degree in a technical or business field, or equivalent practical work experience
- AI Platform Experience: Proven experience managing AI support tools or LLM-based platforms (e.g., Decagon, Happy Robot, Intercom Fin, or similar)
- Established subject matter expert in GTM tooling, with a history of delivering substantial impact across multiple tools
- Ability to translate complex customer and business problems into clear requirements and solutions
- Experience with leveraging AI to solve high-impact business problems
- Analytical Skills: Ability to look at large datasets of conversation logs to identify patterns and root causes of technical failures