Data Scientist, Customer Analytics

Cresta

14d

About The Position

Cresta is on a mission to turn every customer conversation into a competitive advantage by unlocking the true potential of the contact center. Our platform combines the best of AI and human intelligence to help contact centers discover customer insights and behavioral best practices, automate conversations and inefficient processes, and empower every team member to work smarter and faster. Born from the prestigious Stanford AI lab, Cresta's co-founder and chairman is Sebastian Thrun, the genius behind Google X, Waymo, Udacity, and more. Our leadership also includes CEO, Ping Wu, the co-founder of Google Contact Center AI and Vertex AI platform, & co-founder, Tim Shi, an early member of Open AI. We’ve assembled a world-class team of AI and ML experts, go-to-market leaders, and top-tier investors including Andreessen Horowitz, Greylock Partners, Sequoia, and former AT&T CEO John Donovan. Our valued customers include brands like Intuit, Cox Communications, Hilton, and Carmax and we’ve been recognized by Forbes and Bain Consulting as one of the top private AI companies in the world. Join us on this thrilling journey to revolutionize the workforce with AI. The future of work is here, and it's at Cresta. Role Overview: Cresta is expanding its Customer Success organization with a dedicated analytics function focused on customer value realization. As a Data Scientist, Customer Analytics, 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. You’ll sit within the Customer Success organization and partner closely with Business Value, Customer Success Managers, Sales, Product, and Engineering. Your work will power ROI discussions, QBRs, pilots, and renewal conversations. 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.

Requirements

1–3 years of experience in a data-focused role or relevant academic experience
Strong proficiency in SQL and experience working with large datasets
Proficiency in Python (Pandas, NumPy, scikit-learn)
Solid understanding of statistics, hypothesis testing, and experimental design
Experience building dashboards in tools such as Hex, Looker, Tableau, or similar
Strong communication skills with non-technical stakeholders
Comfort working in a fast-paced, cross-functional environment

Nice To Haves

Experience with conversational data (call transcripts, chats) or text analytics
Familiarity with causal inference, uplift modeling, or A/B testing frameworks
Exposure to customer-facing analytics or support roles
Experience with cloud data stacks (Snowflake, Redshift, etc.)
Basic NLP or classification experience related to agent behavior or customer intent

Responsibilities

Conduct exploratory data analysis (EDA) across conversational, operational, and performance datasets
Translate ambiguous business questions into structured analytical problems
Analyze how workflow, behavior, and product usage changes translate into business value
Partner with Business Value consultants on customer-facing insights
Design and analyze A/B tests and quasi-experiments to measure Cresta’s impact
Establish baselines, metrics, and measurement plans for pilots
Ensure results are statistically rigorous and easy for non-technical stakeholders to understand
Build reusable templates and frameworks for consistent experimentation
Build and maintain dashboards for Customer Success, Business Value, and Sales
Develop Python and SQL tools to improve repeatability, accuracy, and scalability
Create standardized reporting packages for pilots, QBRs, and renewals
Develop custom statistical or ML models (e.g., segmentation, predictive scoring, lightweight NLP)
Maintain reusable modeling pipelines for value insights and roadmap analysis
Partner with Engineering when guidance or productionization support is needed
Translate analyses into clear business and financial narratives
Support CSMs with data and insights for strategic QBRs
Partner with Product and Engineering on metrics, data availability, and analytics enhancements