Staff Data Scientist – Core Revenue Retention

HighLevel

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

Posted 9/20/2026full-timeRemote • United StatesLead💰 $163,400 - $220,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in revenue retention analytics, causal inference methods, and data-driven decision-making. Proficient in SQL and Python, with a strong background in B2B SaaS and CPaaS environments, focusing on churn and monetization strategies.

Highest-signal resume keywords

Revenue Retention AnalyticsCausal Inference MethodsSQL ProficiencyPython ProficiencyB2B SaaS Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Causal InferenceChurn AnalysisRetention MetricsData ScrutinyFinancial Data AnalysisRetention FrameworksStatistical AnalysisForecasting InputsAdd-On MonetizationUsage Data Analysis

Soft Skills

Influencing Without AuthorityJudgment in Data InterpretationMentoring Analysts

Tools & Technologies

SnowflakeDbtStatsigAI ToolingClaude

Industry Keywords

CPaaSB2B SaaSSubscription BillingDunningInvoluntary-Churn Recovery

Tech Stack

Tools & technologies

PythonSQL

About the role

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 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

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 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

Comp & perks

Remote-first work environment
Equal Opportunity Employer
Global, remote-first organization
Opportunity to grow a pod as the mandate scales