Staff Data Scientist – Core Revenue Retention
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Job Description
Staff Data Scientist owning causal revenue-retention and add-on monetization analytics. Advising CPaaS, Customer Success, Finance, and product teams at HighLevel, an AI-powered business operating system.
Responsibilities:
- 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 and the drivers behind attach and consumption
- Apply causal inference methods including matching, difference-in-differences, survival/hazard analysis, and synthetic control
- Partner with Finance and RevOps on source-of-truth definitions and forecasting inputs
- Partner with Product Strategy & Growth on the TTP/churn charter and with Experimentation leadership to test retention interventions
- Advise Customer Success, Finance, and Communications/CPaaS leaders
- Set analytical standards for Data Science and adjacent analyst teams
- Set the 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 AI tooling such as Claude for exploration, documentation, and analysis
- Report centrally to Product Analytics & Data Science while carrying the revenue-retention outcome across organizational boundaries
Requirements:
- 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 versus an artifact of how the data was generated
- Experience untangling messy financial, billing, and usage data and defining metrics that withstand scrutiny from Finance and product teams
- Strong SQL and working proficiency in Python
- Comfort in a Snowflake and dbt environment
- Track record of retention or monetization diagnosis changing a product, pricing, Customer Success, or lifecycle decision
- Comfort working amid imperfect, in-progress data and consuming governed sources
- Ability to influence product, Customer Success, Finance, and leadership without direct authority
- CPaaS 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
Benefits:
- Remote-first work environment
- Equal Opportunity Employer
- Global, remote-first organization
- Opportunity to grow a pod as the mandate scales
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