Senior Data Analyst, Customer Operations
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Key skills for this role
About the Role
Scribd is seeking a Senior Data Analyst to build the Customer Success Analytics function and own analytics and operational insights for the Customer Operations department.
Key Skills for This Role
Responsibilities
- Define and maintain core metrics and business definitions across the customer lifecycle, including onboarding milestones, time to value, engagement, customer health, renewals, expansions, and churn
- Build and iterate on dashboards, KPI scorecards, and operational reporting that support day to day execution and executive visibility
- Define and maintain retention metrics, including logo and revenue churn, GRR and NRR, renewal rates, and cohort retention
- Build and operationalize churn and renewal risk analyses and models that surface leading indicators
- Develop and iterate on customer health scoring frameworks that combine usage, lifecycle events, support signals, billing signals, and qualitative inputs
- Build forecasting models for key planning needs such as renewal volume, renewal risk, churn, expansion pipeline, ticket volume, and staffing capacity
- Identify and prototype AI driven workflows that reduce manual analysis and speed up decision making
- Translate Customer Success Operations questions into structured analyses and measurable hypotheses
- Communicate insights with clear narratives that influence decisions across technical and non technical audiences
Requirements
- 4+ years of experience in analytics, business operations, or business intelligence roles, ideally supporting Customer Success, Customer Operations, RevOps, Support, Sales, Growth, or similar customer facing functions
- Experience working in a B2C subscription or membership based business (e.g., SaaS, media, streaming, or consumer subscription), with hands on familiarity with subscription metrics like LTV, churn, refund rate, and renewal rates
- Strong SQL skills and experience working with analytical datasets and BI tools (Looker, Tableau, etc.)
- Comfortable working within an existing Databricks environment, reading gold layer schemas, running queries, and working with Data Engineering
- Experience with Python (or similar) for analysis, forecasting, and modeling
- A track record of building retention, churn, renewal risk, forecasting, or related analyses and translating outputs into business action
- A strong foundation in statistics and experimental thinking, including hypothesis testing and measurement design
- Strong communication skills, with the ability to influence stakeholders across technical and non technical teams
- Comfort working independently in an environment with evolving priorities
Full Job Posting
About the Team and Role
- Scribd is evolving from one of the world’s largest document libraries into a platform where active knowledge building happens.
- As the first Senior Data Analyst embedded in Customer Operations you will have the unique opportunity to build the CS Analytics function and own analytics and operational insights for the department.
- You will partner closely with Customer Operations leadership & team, Product, Finance (RevOps), and Data Engineering to build trusted reporting, improve operational rhythms, and drive measurable outcomes across retention, expansion, customer health, and support efficiency.
- In this role, you will translate ambiguous questions into clear measurement frameworks, forecasting models, and actionable reporting.
- You will also help the organization adopt AI enabled analytics and automation to reduce manual work, improve decision quality, and establish strong measurement and governance.
You will
- Own Customer Success and Customer Operations measurement: Define and maintain core metrics and business definitions across the customer lifecycle.
- Create clear documentation and enable consistent interpretation across Customer Success Operations, RevOps, Finance.
- Establish instrumentation and data quality requirements with Data Engineering to ensure reliable sources of truth.
- Build decision ready reporting and self serve analytics: Build and iterate on dashboards, KPI scorecards, and operational reporting.
- Enable self serve analytics with clear definitions, drill paths, and actionable views for CS leaders, managers, and operators.
- Create automated reporting and proactive alerting for KPI movement and risk signals.
- Customer retention, churn, and expansion analytics: Define and maintain retention metrics, including logo and revenue churn, GRR and NRR, renewal rates, and cohort retention.
- Build and operationalize churn and renewal risk analyses and models that surface leading indicators.
- Develop and iterate on customer health scoring frameworks that combine usage, lifecycle events, support signals, billing signals, and qualitative inputs.
- Forecasting and capacity planning: Build forecasting models for key planning needs such as renewal volume, renewal risk, churn, expansion pipeline, ticket volume, and staffing capacity.
- Define evaluation approaches such as backtesting, holdouts, calibration, and monitoring, and ensure forecasts remain reliable over time.
- Partner with Customer Operations to translate forecasts into staffing plans, coverage models, and operating cadences.
You have
- 4+ years of experience in analytics, business operations, or business intelligence roles, ideally supporting Customer Success, Customer Operations, RevOps, Support, Sales, Growth, or similar customer facing functions.
- Experience working in a B2C subscription or membership based business (e.g., SaaS, media, streaming, or consumer subscription), with hands on familiarity with subscription metrics like LTV, churn, refund rate, and renewal rates.
- Strong SQL skills and experience working with analytical datasets and BI tools (Looker, Tableau, etc.), with an emphasis on performance, usability, and metric governance.
- Comfortable working within an existing Databricks environment, reading gold layer schemas, running queries, and working with Data Engineering to understand what data is available and how to use it.
- Experience with Python (or similar) for analysis, forecasting, and modeling.
- A track record of building retention, churn, renewal risk, forecasting, or related analyses and translating outputs into business action.
- A strong foundation in statistics and experimental thinking, including hypothesis testing and measurement design.
- Strong communication skills, with the ability to influence stakeholders across technical and non technical teams.
- Comfort working independently in an environment with evolving priorities.
Nice to have
- Experience with customer health scoring, churn modeling, retention and expansion analytics, or lifecycle analytics.
- Experience with analytics engineering practices (for example dbt style testing, documentation, and semantic layers).
- Experience evaluating or implementing AI or LLM enabled analytics workflows, including quality measurement and human in the loop processes.
- Familiarity with SaaS subscription metrics, cohort analysis, and billing systems.
- Proficiency with Zendesk or similar customer support platforms, and comfort working directly in support tooling to extract and analyze operational data.
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