Staff Data Scientist - Core Revenue Retention

About the role

Staff Data Scientist - Core Revenue Retention, fully remote within the United States. The role owns the analytics strategy for retaining and growing revenue from existing customers across the business, analyzing churn, retention, subscription revenue, usage, and add-on monetization across communications and emerging AI-powered revenue streams. It connects Product, Customer Success, Finance, Revenue Operations, and Data Science around a consistent view of revenue health, builds rigorous models to distinguish genuine retention signals from billing, data-quality, seasonality, and customer-mix effects, influences senior leaders, establishes company-wide measurement standards, and shapes strategic retention and monetization priorities.

Responsibilities

• Own the analytical and causal assessment of core revenue retention and add-on monetization, including gross and net revenue retention, MRR churn, voluntary and involuntary churn, add-on attachment, and usage.
• Analyze retention and monetization across communications, messaging, telephony, AI-powered add-ons, and other revenue-generating product areas.
• Quantify opportunities for add-on revenue growth and identify the behavioral, product, pricing, and customer factors that influence attachment and consumption.
• Apply rigorous causal-inference techniques when controlled experiments are not feasible, including matching, difference-in-differences, survival and hazard analysis, and synthetic controls.
• Distinguish genuine retention and monetization signals from selection bias, seasonality, customer mix, billing artifacts, and other characteristics of the underlying data.
• Partner with Finance and Revenue Operations to establish consistent definitions, trusted reporting, and reliable inputs for revenue-retention forecasting.
• Collaborate with Product Strategy and Growth teams on churn and trial-to-paid initiatives and partner with experimentation specialists to evaluate retention interventions rigorously.
• Serve as a trusted analytical advisor to Customer Success, Finance, Communications, and product leaders, translating complex findings into clear recommendations.
• Establish analytical standards that Data Science and Analytics teams can adopt across the