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AI Summary
The role involves designing and maintaining an AI technology stack to support automated customer lifecycles and product-led engagement, unifying customer data, and implementing AI tools to resolve friction points in conversion processes. Responsibilities also include implementing AI-driven user experiences, developing expansion strategies, and scaling operations through automation.
About the role:
- Key Responsibilities
AI Infrastructure & Platform Strategy
- Design and maintain an AI technology stack that supports automated customer life cycles and product-led engagement.
- Unify customer data into a definitive system of record to power all automated communications and buyer journeys.
- Evaluate and implement AI tools and data constructs that solve specific friction points in the freemium and trial conversion process.
- Establish data pipelines and model governance to ensure reliable, scalable, and automated insights.
PLG AI Enablement & Support
- Implement AI-driven experiences within the application to communicate directly with users based on real-time behavior.
- Develop AI-powered health monitoring and expansion strategies to drive engagement beyond the initial sales meeting.
- Scale operations through automation, focusing on "human-in-the-loop" workflows rather than managing a large manual team.
- Provide recommendations to product and marketing teams on building the foundation for new, automated GTM motions.
Cross-Functional AI Strategy
- Serve as the AI subject matter expert, "charting the path" for growth by translating business requirements into technical AI solutions.
- Conduct a 90-day "listening tour" to map out current onboarding processes, data availability, and build customer empathy.
- Establish performance metrics and continuously optimize AI models based on business outcomes and ROI.
Required Experience
- 5+ years in revenue, sales, or marketing operations with recent, hands-on experience implementing AI tools.
- PLG Background: Proven experience at a Product-Led Growth company, ideally at the Series C stage.
- Technical Acumen: Strong background in data analysis, aligning data models, and evaluating model performance.
- Mindset: Data-driven and experimentation-minded with specific experience in freemium/trial conversion models.
- Execution: A "scrappy" approach to execution with the ability to manage vendor relationships and lead change management.
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