Product Manager - AI Chat Bot
AI Summary
Product Manager responsible for end-to-end lifecycle of internal CS management systems, focusing on QA, performance, learning/training, and notification centers. Uses AI/automation to optimize workflows and collaborates with CS, QA, Training, and Compliance.
About this role
Responsibilities
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Product Planning & Lifecycle Management: Responsible for the end-to-end product lifecycle of Customer Service (CS) management systems, specifically focusing on Quality Assurance (QA), Performance Management, Learning & Training systems, and Notification Centers.
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Multi-Channel Support Optimization: Maintain and enhance multi-channel service tools (Ticketing System, Email, Whistleblowing, Outbound), ensuring stability and operational efficiency for global CS teams.
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Efficiency & Intelligence: Leverage AI and automation to upgrade traditional management workflows. Examples include AI-assisted QA scoring, automated performance reporting, and smart task distribution.
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Stakeholder Collaboration: Partner closely with CS Management, QA, Training, and Compliance teams to identify operational bottlenecks and translate complex business requirements into scalable product solutions.
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Data-Driven Iteration: Monitor key usage metrics of internal tools (e.g., system adoption rate, processing time) and utilize data insights to drive continuous product optimization and reduce administrative overhead.
Requirements
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Bachelor’s degree or above; 3-5 years of product management experience, preferably in CRM, SaaS, B2B internal tools, or Contact Center systems.
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Domain Knowledge: Familiarity with CS operations workflows (QA, Training, Performance) or ticketing systems (e.g., Zendesk, Salesforce) is a strong plus.
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Logical & System Thinking: Strong ability to design complex workflows and permission systems. Capable of handling intricate logic behind backend management consoles.
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Communication: Excellent communication skills to align with multiple internal stakeholders (Operations, Legal, Compliance, Dev) in a fast-paced global environment.
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Data & AI Sensitivity: Proficiency in data analysis and a good understanding of how to apply LLM/AI to internal operational scenarios.
Skills
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