Customer Success Manager

📋 Description

• • As a Customer Success Manager at Wisdom AI, you will be instrumental in driving transformative change for our enterprise clients by architecting and executing successful deployments of our cutting-edge generative AI data analysis platform. This role transcends traditional customer management; you will act as a Deployment Strategist, guiding customers from the initial stages of data integration and use-case discovery through sophisticated Context Modeling and ultimately, to widespread enterprise adoption. Your primary objective is to ensure that Wisdom AI not only functions flawlessly but delivers profound 'Wisdom' that empowers our clients to achieve their most critical business objectives.
• • **Mission Ownership & Strategic Vision:** You will take full ownership of the holistic health and return on investment (ROI) for your assigned customer portfolio. This involves identifying and prioritizing the 'North Star' use cases that will have the most significant positive impact on their business operations and strategic goals. You'll be responsible for understanding their business landscape deeply and aligning Wisdom AI's capabilities to drive measurable outcomes.
• • **Hands-On Deployment & Technical Leadership:** A core aspect of this role involves leading the technical setup and configuration of the Wisdom AI platform. This includes the crucial task of connecting diverse data sources such as Snowflake, BigQuery, and others, meticulously modeling domain-specific knowledge to enhance AI understanding, and constructing the 'Context Layer' that forms the bedrock of the AI's analytical power. You will be the technical point person, ensuring seamless integration and optimal performance.
• • **In-Depth Use-Case Discovery & Scoping:** You will conduct detailed 'Scoping Sessions' with various customer stakeholders, from technical teams to business leaders. The goal is to deconstruct complex business challenges and ambiguous data landscapes into specific, actionable data questions that Wisdom AI can effectively address. This requires strong analytical and consultative skills to uncover latent needs and opportunities.
• • **Data Modeling & Continuous Optimization:** Your responsibilities extend to refining prompt patterns to elicit the most accurate and relevant responses from the AI. You will rigorously validate answer accuracy, develop and implement custom metrics, and ensure the AI consistently communicates in the customer's unique business language. This iterative process is key to maximizing the platform's utility and precision.
• • **Product Feedback & Engineering Collaboration:** You will serve as a 'forward-deployed' product manager, acting as the crucial bridge between customer needs and our product development team. You will be responsible for identifying bugs, gathering user feedback on friction points, and proactively suggesting new features and enhancements. Close collaboration with the Engineering team is essential to drive continuous improvement of the core platform based on real-world usage.
• • **Driving Adoption & User Enablement:** A significant part of your role will be to foster widespread adoption of Wisdom AI within customer organizations. This involves training 'Power Users' to independently build their own 'Stories' and dashboards within the platform, effectively transforming them into internal champions and advocates for Wisdom AI. This enablement strategy is key to scaling the platform's impact.
• • **Demonstrating Tangible Value Realization:** Ultimately, your success will be measured by the demonstrable, measurable impact you deliver to customers. Whether it's significantly reducing the workload on internal data teams, accelerating executive decision-making cycles, or uncovering new revenue opportunities, you must consistently prove the platform's value proposition through concrete results.
• • **Navigating Technical Documentation & Platform Features:** You will be expected to become an expert in the Wisdom AI platform, including navigating and executing tasks outlined in the comprehensive Wisdom AI Documentation. This includes advanced configurations such as connecting unstructured repositories, setting up Row Level Access Control (RLAC) for data security, integrating with communication tools like Slack, embedding Wisdom AI insights via GraphQL APIs, and managing organizational structures and data domains within the platform.