Staff Data Scientist - Core Revenue Retention

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.

Accountabilities
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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.

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

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

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

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

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

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

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

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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.

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

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

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

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

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

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

Requirements

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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.

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Strong practical experience with causal inference and sound judgment rega