Staff Data Scientist - Core Revenue Retention

HighLevel

2d • $163,400 - $220,000 • Remote

About The Position

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.

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 Haves

• 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

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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed