Staff Data Scientist

HighLevel; Texas; Hybrid; USD 170,000 - 210,000; Full time

Job summary

HighLevel is seeking a Staff Data Scientist, Core Revenue Retention, to own the revenue-retention outcome across CPaaS, add-ons, and Success motions. You’ll build the retention model, separate signal from noise, and set standards adopted across analytics teams.

This hands-on, cross-functional role reports to Product Analytics and Data Science and scales the mandate as it proves impact. The role requires deep experience in churn, retention monetization, SQL/Python, and governance of data sources,

Qualifications

• 9+ years in revenue/retention analytics, data science, or applied statistics.
• Practical causal inference with sound judgment about when a result is causal.
• Comfort untangling messy financial/billing/usage data and defining robust metrics.
• Strong SQL and Python; experience with Snowflake + dbt.
• Track record of affecting product/pricing, CS, or lifecycle decisions.
• Ability to influence cross-functionally without direct authority.
• Experience with governance of data sources and adoption across teams.

Responsibilities

• Own the causal read on core revenue retention and add-on monetization across CPaaS and related surfaces.
• Quantify add-on revenue opportunity and drivers of attachment and usage.
• Apply rigorous causal inference methods where clean experiments aren’t feasible.
• Partner with Finance/RevOps on single-source-of-truth definitions and forecasts.
• Collaborate with Product Strategy on churn-related initiatives and growth charter.
• Advise leadership and set analytic standards adopted by DS teams.
• Define the canonical retention metrics and data taxonomy across governed data.
• Build reusable retention and causal-inference frameworks for analytics teams.

Skills

SQL

Python

Causal inference

Revenue analytics

Cross-functional collaboration

Data storytelling

AI tooling

Experimentation

Analytics strategy

Churn/retention monetization

Tools

dbt

Snowflake

Statsig

Claude-like AI tooling

Job description

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 and Data Science for craft and standards and carry the revenue-retention outcome across organizational boundaries. You'll build the retention/value model, separate the 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.

Responsibilities:

• Own the causal read on core revenue retention and add-on monetization: gross and net revenue retention, MRR churn (voluntary vs. involuntary), and attachment 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 attachment 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 and 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 that depends on Analytics Engineering.
• Build the retention and causal-inference framework and 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.

Success in this role looks like:

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