Staff Data Scientist - Core Revenue Retention

Accountabilities

• • Own the analytical and causal assessment of core revenue retention and add-on monetization, including gross and net revenue retention, MRR churn, voluntary and involuntary churn, add-on attachment, and usage.

  • Analyze retention and monetization across communications, messaging, telephony, AI-powered add-ons, and other revenue-generating product areas.

  • Quantify opportunities for add-on revenue growth and identify the behavioral, product, pricing, and customer factors that influence attachment and consumption.

  • Apply rigorous causal-inference techniques when controlled experiments are not feasible, including matching, difference-in-differences, survival and hazard analysis, and synthetic controls.

  • Distinguish genuine retention and monetization signals from selection bias, seasonality, customer mix, billing artifacts, and other characteristics of the underlying data.

  • Partner with Finance and Revenue Operations to establish consistent definitions, trusted reporting, and reliable inputs for revenue-retention forecasting.

  • Collaborate with Product Strategy and Growth teams on churn and trial-to-paid initiatives and partner with experimentation specialists to evaluate retention interventions rigorously.

  • Serve as a trusted analytical advisor to Customer Success, Finance, Communications, and product leaders, translating complex findings into clear recommendations.

  • Establish analytical standards that Data Science and Analytics teams can adopt across the organization, raising the quality and consistency of retention measurement without relying on direct authority.

  • Set the technical direction for company-wide revenue-retention measurement, including canonical GRR, NRR, churn, and add-on metrics.

  • Work with governed and certified data sources while collaborating with Analytics Engineering to improve the taxonomy and data foundation required for retention analytics.

  • Build reusable retention and causal-inference frameworks and methodologies that can be adopted by Analytics Engineering and adjacent Data Science teams.

  • Use AI-assisted tools to accelerate analytical exploration, documentation, analysis, and other workflows.

  • Translate retention diagnoses into evidence-based priorities that can inform product, pricing, Customer Success, lifecycle, and investment decisions.

  • Establish scalable analytical patterns and foundations that enable the revenue-retention mandate to expand beyond a single individual contributor.

  Requirements

  • 9+ years of experience in revenue analytics, retention analytics, data science, applied statistics, or a related field, with deep expertise in churn, retention, and monetization.

  • Strong practical experience with causal inference and sound judgment regarding when analytical findings represent genuine causal relationships versus artifacts of how data was generated.

  • Demonstrated ability to work with complex financial, billing, subscription, and usage data and establish metrics that withstand scrutiny from both Finance and Product stakeholders.

  • Strong SQL skills and working proficiency in Python.

  • Experience working in modern data environments involving Snowflake, dbt, or comparable technologies.

  • Proven track record of turning retention or monetization analysis into decisions affecting product strategy, pricing, Customer Success, lifecycle programs, or other business initiatives.

  • Experience working effectively with imperfect or evolving data foundations, using governed sources while identifying opportunities to improve data quality and analytical standards.

  • Strong cross-functional influence, with the ability to align Product, Customer Success, Finance, Revenue Operations, and leadership teams around shared metrics and conclusions without direct authority.

  • Excellent communication skills and the ability to translate sophisticated statistical and analytical findings into clear business recommendations for technical and executive audiences.

  • Strategic, analytical, and pragmatic approach, with the ability to operate independently across broad organizational boundaries.

  • Experience with CPaaS, telephony, messaging, or usage-based and consumption-based revenue models is a plus.

  • B2B SaaS or CRM experience is desirable, particularly exposure to MRR, subscription billing, dunning, and involuntary-churn recovery.

  • Familiarity with Statsig or a comparable experimentation platform is advantageous.

  • Experience with AI-assisted analytics workflows and/or mentoring analysts is a plus.

  Benefits

  • Annual salary range of $163,400–$220,000, depending on experience and role-related qualifications.

  • Fully remote work environment within the United States.

  • Staff-level ownership of a company-wide revenue-retention and monetization analytics mandate.

  • Opportunity to work directly with Product, Customer Success, Finance, Revenue Operations, Communications, and Data Science teams.

  • Significant autonomy to define analytical standards, measurement frameworks, and reusable causal-inference methodologies.

  • Opportunity to advise senior leaders and influence product, pricing, Customer Success, and investment decisions through rigorous analysis.

  • Opportunity to work on complex subscription, usage, billing, retention, and add-on monetization challenges at scale.

  • Exposure to modern data infrastructure and governed analytical data sources.

  • Opportunity to leverage AI-assisted tools to accelerate analytical research, documentation, and decision-making.

  • Potential to grow and lead an analytics pod as the revenue-retention mandate expands.

  • Equal opportunity employment environment with voluntary demographic information collected separately for applicable compliance and reporting purposes.