AI Value Partner, Customer Analytics

About this role

<div class="content-intro"><p>Cresta unlocks the true potential of the customer experience, turning every conversation into a competitive advantage. Cresta’s unified AI platform combines conversational AI agents, real-time human agent augmentation, and comprehensive conversation intelligence to drive revenue and efficiency gains across every channel. The world’s leading companies, including United Airlines, Cox Communications, and Marriott, use Cresta to power world-class customer experiences every day. </p> <p>Born from the Stanford AI Lab, Cresta has raised more than $270 million from the world’s leading investors, including a16z, Greylock, and Sequoia. Cresta’s leadership includes some of the leading minds in AI today. Our CEO, <a href="https://www.linkedin.com/in/pingwu/">Ping Wu</a>, founded and led Google's Contact Center AI and Vertex AI platforms before joining Cresta to build the future of AI-driven customer experiences.</p> <p>Over the next few years, AI is going to redefine how people all over the world interact with businesses every day. Come build that future at Cresta.</p></div><p> </p> <p><strong>Role Overview:</strong></p> <p>Cresta is expanding its Customer Success organization with a dedicated analytics function focused on customer value realization. As an <strong>AI Value Partner, Customer Analytics</strong>, you’ll be the technical engine behind measuring and storytelling customer impact—designing experiments, analyzing conversational and operational data, and building dashboards that quantify Cresta’s value.</p> <p>You’ll sit within the Customer Success organization and partner closely with <strong>Customer Success Managers, Sales, Product, and Engineering</strong>. Your work will power ROI discussions, pilots, QBRs and ongoing strategic customer engagements.</p> <p>This role is ideal for someone early in their data science career who enjoys hands-on analytics, problem-solving, and turning ambiguous business questions into clear, actionable insights.</p> <p><strong>Key Responsibilities:</strong></p> <p><strong>Customer Analytics & Insight Generation</strong></p> <ul> <li>Conduct exploratory data analysis (EDA) across conversational, operational, and performance datasets</li> <li>Translate ambiguous business questions into structured analytical problems</li> <li>Analyze how workflow, behavior, and product usage changes translate into business value</li> </ul> <p><strong>Experimentation & Pilot Measurement</strong></p> <ul> <li>Design and analyze A/B tests and quasi-experiments to measure Cresta’s impact</li> <li>Establish baselines, metrics, and measurement plans for pilots</li> <li>Ensure results are statistically rigorous and easy for non-technical stakeholders to understand</li> <li>Build reusable templates and frameworks for consistent experimentation</li> <li>Work directly with customer to guide them towards our ideal experiment setup</li> </ul> <p><strong>Dashboards, Reporting & Automation</strong></p> <ul> <li>Build and maintain dashboards tracking customer ROI</li> <li>Develop Python and SQL tools to improve repeatability, accuracy, and scalability</li> <li>Create standardized reporting packages for pilots, QBRs, and renewals</li> </ul> <p><strong>Modeling & Advanced Analytics</strong></p> <ul> <li>Develop custom statistical or ML models (e.g., segmentation, predictive scoring, lightweight NLP)</li> <li>Maintain reusable modeling pipelines for value insights and roadmap analysis</li> <li>Partner with Engineering when guidance or productionization support is needed</li> </ul> <p><strong>Cross-Functional Collaboration</strong></p> <ul> <li>Translate analyses into clear business and financial narratives</li> <li>Support CSMs with data and insights for strategic QBRs</li> <li>Partner with Product and Engineering on metrics, data availability, and analytics enhancements</li> </ul> <p><strong>Required Qualifications:</strong></p> <ul> <li>1–3 years of experience in a data-focused role or relevant academic experience</li> <li>Strong proficiency in SQL and experience working with large datasets</li> <li>Proficiency in Python (Pandas, NumPy, scikit-learn)</li> <li>Solid understanding of statistics, hypothesis testing, and experimental design</li> <li>Experience building dashboards in tools such as Hex, Looker, Tableau, or similar</li> <li>Strong communication skills with non-technical stakeholders</li> <li>Comfort working in a fast-paced, cross-functional environment</li> </ul> <p><strong>Preferred Qualifications:</strong></p> <ul> <li>Experience with conversational data (call transcripts, chats) or text analytics</li> <li>Familiarity with causal inference, uplift modeling, or A/B testing frameworks</li> <li>Exposure to customer-facing analytics or support roles</li> <li>Experience with cloud data stacks (Snowflake, Redshift, etc.)</li> <li>Basic NLP or classification experience related to agent behavior or customer intent</li> </ul> <p><strong>Success Criteria:</strong></p> <p>Within 6–9 months, a successful hire will have:</p> <ul> <li>Built dashboards and reporting foundations…