Staff Data Scientist - Core Revenue Retention

About HighLevel: HighLevel is an AI-powered business operating system that gives agencies, entrepreneurs and SMBs the infrastructure to build, automate and scale. Today, HighLevel supports SMBs across 150+ countries, fueling community-driven growth rooted in real customer outcomes. To date, businesses operating on HighLevel have generated over $7 billion in ecosystem value, demonstrating the impact of shared infrastructure at scale. By centralizing conversations, automation and intelligence into one system, we help businesses move faster, reduce complexity and execute efficiently. Behind the platform, HighLevel powers more than 4 billion API hits and 2.5 billion message events daily. With 250 terabytes of distributed data, 250+ microservices and over 1 million domain names supported, our architecture is built for performance, resilience and long-term scalability.

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We're hiring a Staff Data Scientist, Core Revenue Retention to own one outcome — keeping and growing revenue from existing customers — across every team that shapes it. Revenue retention isn't the property of a single product: it spans CPaaS (phone, SMS, email, WhatsApp) as our largest MRR surface, add-on monetization including emerging AI features, the Customer Success motions that protect accounts, and Finance's forecasts. This is a broad, cross-functional role. You'll work closely with Communications/CPaaS, Customer Success, the AI teams pursuing add-on revenue, Finance, and other revenue-driving product teams; you report centrally to Product Analytics & Data Science for craft and standards and carry the revenue-retention outcome across organizational boundaries. You'll build the retention/value model, separate real churn signal from data-maturity and mix artifacts, and turn diagnosis into a prioritized, evidence-based retention and add-on-monetization agenda. You'll work amid a data foundation still being built, consuming governed sources rather than rebuilding them, and raising the bar as you go. This is a hands-on, direction-setting Staff role — you advise Customer Success, Finance, and CPaaS leaders and set retention-measurement standards that analysts on adjacent teams adopt, with a path to grow a pod as the mandate scales.

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

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

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

CPaaS, AI add-ons, and Customer Success act on your model, and drives strong positive business results. Finance/RevOps and Product Analytics report the consistent metrics with clear insight and recommendations. Leaders across the revenue domain make roadmap and spend calls off your analysis, not gut feel The revenue-retention mandate has reusable patterns and the foundation to scale beyond one IC

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