Staff Data Scientist - Core Revenue Retention in New York at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Data Scientist - Core Revenue Retention based in the United States.

The Staff Data Scientist will own the analytics strategy for retaining and growing revenue from existing customers across the business.
You’ll analyze churn, retention, subscription revenue, usage, and add-on monetization across communications and emerging AI-powered revenue streams.
The role connects Product, Customer Success, Finance, Revenue Operations, and Data Science around a consistent view of revenue health.
You’ll build rigorous models that distinguish genuine retention signals from billing, data-quality, seasonality, and customer-mix effects.
As a Staff-level individual contributor, you’ll influence senior leaders, establish company-wide measurement standards, and shape strategic retention and monetization priorities.
You’ll work with governed data in an evolving analytics environment while strengthening reusable methods and analytical foundations.
This is a hands-on, high-impact opportunity with potential to grow a dedicated analytics pod as the mandate expands.

• 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.

• 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.

• 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.