Staff Data Scientist – Core Revenue Retention

This is a fully remote position, open to applicants in India.

📋 Description

• Take ownership of analyzing core revenue retention and add-on monetization across CPaaS, AI add-ons, and other revenue streams.

• Assess the revenue potential of add-ons within CPaaS and new AI features, identifying the factors influencing attachment and usage.

• Implement causal inference techniques such as matching, difference-in-differences, survival/hazard analysis, and synthetic control.

• Collaborate with Finance/RevOps to define a single source of truth and provide forecasting inputs.

• Work alongside Product Strategy & Growth on the TTP/churn initiative and cooperate with Experimentation to evaluate retention strategies.

• Provide guidance to leaders in Customer Success, Finance, and Communications/CPaaS.

• Establish analytical benchmarks for data science and analysts in related teams.

• Define the technical approach for organization-wide revenue-retention metrics and oversee canonical GRR/NRR, churn, and add-on measures.

• Develop the retention analytics taxonomy in collaboration with Analytics Engineering.

• Create reusable frameworks for retention analysis and causal inference.

• Utilize Claude and similar AI tools for exploration, documentation, and analysis.

⛳️ Requirements

• Over 9 years of experience in revenue/retention analytics, data science, or applied statistics, with significant expertise in churn, retention, and monetization.

• Practical knowledge of causal inference with a keen understanding of distinguishing causal results from artifacts of data generation.

• Proficient in navigating complex financial, billing, and usage data, along with defining metrics that can withstand scrutiny from both Finance and product teams.

• Strong SQL skills and a working knowledge of Python.

• Familiarity with a Snowflake + dbt environment.

• Proven history of making impactful retention or monetization recommendations that influenced product, pricing, customer success, or lifecycle decisions.

• Able to work effectively with incomplete, evolving data by leveraging governed sources and enhancing standards without the need to rebuild data pipelines.

• Ability to influence cross-functionally among product, Customer Success, Finance, and leadership teams without direct authority.

• Experience in CPaaS (telephony/messaging) or consumption-based revenue models.

• Background in B2B SaaS or CRM, with familiarity in MRR/subscription billing, dunning processes, and involuntary churn recovery.

• Knowledge of Statsig or a similar experimentation platform.

• Exposure to AI-driven analytics workflows.

• Experience mentoring analysts.

🏝️ Benefits

• Global, remote-first organization.

• Opportunity to collaborate with a diverse team across more than 10 countries.

• Potential for career growth as the team's mandate expands.