Staff Data Scientist – Core Revenue Retention

. Own the causal read on core revenue retention and add-on monetization across CPaaS, AI add-ons, and other revenue surfaces .

Posted 9/26/2026full-timeRemote • IndiaLeadWebsite

Tools & technologies

PythonSQL

Key responsibilities & impact

Own the causal read on core revenue retention and add-on monetization across CPaaS, AI add-ons, and other revenue surfaces
Quantify add-on revenue opportunity across CPaaS and emerging AI features, and identify drivers behind attach and consumption
Apply causal inference methods including matching, diff-in-diff, survival/hazard, and synthetic control
Partner with Finance/RevOps on single-source-of-truth definitions and forecasting inputs
Partner with Product Strategy & Growth on the TTP/churn charter and with Experimentation to test retention interventions
Advise Customer Success, Finance, and Communications/CPaaS leaders
Set analytical standards for data science and analysts on adjacent teams
Set technical direction for company-wide revenue-retention measurement and own canonical GRR/NRR, churn, and add-on metrics
Shape the retention analytics taxonomy with Analytics Engineering
Build reusable retention and causal-inference frameworks
Use Claude and similar AI tooling for exploration, documentation, and analysis

What you’ll need

9+ years in revenue/retention analytics, data science, or applied statistics, with deep experience on churn, retention, and monetization
Practical causal inference with sound judgment about when a result is causal vs. an artifact of how the data was generated
Comfort untangling messy financial/billing/usage data and defining metrics that survive scrutiny from Finance and product alike
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 lifecycle decision
Comfort amid imperfect, in-progress data — consuming governed sources and raising the bar rather than rebuilding pipelines
Cross-functional influence across product, Customer Success, Finance, and leadership without direct authority
CPaaS (telephony/messaging) or usage-based/consumption revenue experience
B2B SaaS or CRM background; experience with MRR/subscription billing, dunning, and involuntary-churn recovery
Familiarity with Statsig or a comparable experimentation platform
Exposure to AI-assisted analytics workflows
Experience mentoring analysts

Comp & perks