Posted today · be early
Staff Data Scientist - Core Revenue Retention
HighLevel
IndiaremotePosted today
Skill Required
Data-ScientistRevenue-AnalystBusiness-Intelligence-AnalystAnalytics-EngineerRetention-AnalystStaff-Data-ScientistSenior-Staff-Data-ScientistRevenue-Data-ScienceSenior-Staff-Data-ScienceData ScientistMicroservicesEngineeringStatisticsanalyticalautomationSnowflakesimilar)analyticsbuildingPythondbtSQLandGoAIFulltime
Key highlights
- Required experience: 9+ years
- Key benefit: Global, remote-first organization
- Notable requirement: Proficiency in Snowflake + dbt
Role overview
HighLevel is an AI-powered business operating system supporting SMBs across 150+ countries. The company operates as a global, remote-first organization that centralizes conversations, automation, and intelligence into one system to help businesses scale. We're hiring a Staff Data Scientist, Core Revenue Retention to own the outcome of keeping and growing revenue from existing customers. This is a broad, cross-functional, hands-on, direction-setting Staff role reporting to Product Analytics & Data Science, working across CPaaS, Customer Success, AI teams, Finance, and other revenue-driving product teams to build retention/value models and turn diagnosis into a prioritized, evidence-based agenda.
Responsibilities
- Own the causal read on core revenue retention and add-on monetization — gross and net revenue retention, MRR churn (voluntary vs involuntary), attach and usage of add-ons — across CPaaS, AI add-ons, and other revenue surfaces
- Quantify add-on revenue opportunity across CPaaS and emerging AI features, and the drivers behind attach and consumption
- Apply rigorous causal inference (matching, diff-in-diff, survival/hazard, synthetic control) where clean experiments aren't feasible — separating real signal from selection bias, seasonality, and mix
- Partner with Finance/RevOps on single-source-of-truth definitions and forecasting inputs; drive the revenue-retention insights
- Partner with the Product Strategy & Growth org on the TTP/churn charter, and with the Experimentation lead to test retention interventions rigorously
- Act as a trusted analytical advisor to Customer Success, Finance, and Communications/CPaaS leaders, and set the analytical standards that DS and analysts on adjacent teams adopt — raising the bar without direct authority
- Set the technical direction for how revenue retention is measured company-wide — own the canonical GRR/NRR, churn, and add-on metrics on governed, certified data that other teams build on; shape the taxonomy retention analytics depends on with Analytics Engineering
- Build the retention and causal-inference framework — the standards and reusable methods (survival/hazard, diff-in-diff, synthetic control) that Analytics Engineering and adjacent DS teams reuse beyond this mandate
- Use AI tooling (Claude and similar) to move faster on exploration, documentation, and analysis
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 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 — you consume governed sources and raise the bar rather than rebuilding pipelines
- Cross-functional influence — you align product, Customer Success, Finance, and leadership on shared numbers without direct authority
Nice to have
- 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
Additional details
- Success in this role looks like: CPaaS, AI add-ons, and Customer Success act on your model, and drives strong positive business results.
- Success in this role looks like: Finance/RevOps and Product Analytics report the consistent metrics with clear insight and recommendations.
- Success in this role looks like: Leaders across the revenue domain make roadmap and spend calls off your analysis, not gut feel
- Success in this role looks like: The revenue-retention mandate has reusable patterns and the foundation to scale beyond one IC
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- Originally posted on Himalayas
