Description
📋 Description
•Own the causal read on core revenue retention and add-on monetization — gross and net revenue
•Quantify add-on revenue opportunity across CPaaS and emerging AI features, and the drivers behind
•Apply rigorous causal inference (matching, diff-in-diff, survival/hazard, synthetic control) where
•Partner with Finance/RevOps on single-source-of-truth definitions and forecasting inputs; drive the
•Partner with the Product Strategy & Growth org on the TTP/churn charter, and with the
•Act as a trusted analytical advisor to Customer Success, Finance, and Communications/CPaaS leaders
🎯 Requirements
•9+ years in revenue/retention analytics, data science, or applied statistics, with deep experience
•Practical causal inference with sound judgment about when a result is causal vs. an artifact of how
•Comfort untangling messy financial/billing/usage data and defining metrics that survive scrutiny
•Strong SQL and working proficiency in Python; comfort in a Snowflake + dbt environment
•Track record where a retention or monetization diagnosis changed a product, pricing, CS, or
•Comfort amid imperfect, in-progress data — you consume governed sources and raise the bar rather
