Function:

Data Science & Analytics - Data Analysis / Business Intelligence, Data Science / Machine Learning, Other Data Science / Analytics

Python, SQL, Snowflake, dbt

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 in 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 consistent metrics with clear insight and recommendations.

- Leaders across the revenue domain make roadmaps and spend calls based on your analysis, not gut feeling.

- The revenue-retention mandate has reusable patterns and the foundation to scale beyond one IC.