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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Analyst, Customer Operations Organization - **Company:** Scribd Inc. - **Location:** San Francisco, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $97,000.0 - $146,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Software as a Service, Information Engineering, Data Files, Database Queries, Statistical Hypothesis Testing, Python (Programming Language), Operational Data Store, SQL Databases, Data Streaming, Tableau (Software), Scripting, Large Language Models, Data Layers, Video Streaming, Zendesk, Looker Analytics, Surface Modeling, Databricks - **Published:** August 31, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-data-analyst-customer-operations-san-francisco-ca--c1e6c3df-2172-4d2b-b7ca-35c281bd958f ## About the Role You are a deeply analytical thinker who is curious and loves to solve problems. You are comfortable operating in a fast-moving environment with evolving priorities. You combine strong technical skills with an operator's mindset and can communicate clearly with both technical and non-technical partners., * 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. * ---------------------------------------------------------------------------------, Analysis Skills, Artificial Intelligence (AI), Automation, Benchmarking, Billing, Business Intelligence, Business Intelligence Software, Business Operations, Calibration, Capacity Management, Communication Skills, Compensation Management, Cross-Functional, Customer Churn, Customer Relations, Customer Retention/Renewal, Customer Support/Service, Customer/Client Research, Data Analysis, Data Quality, Data Sets, Documentation, Embedded Systems, Establish Priorities, Finance, Forecasting, Instrumentation, Leadership, LifeTime Value (LTV), Looker, Machine Tool, Metrics, Onboarding, Operational Audit, Operational Improvement, Operational Measurement, Operational Support, Performance Metrics, Problem Solving Skills, Prototyping, Python Programming/Scripting Language, Quality Management, Quality Metrics, Reporting Dashboards, Reporting Skills, Revenue Growth, Risk, Risk Analysis, Risk Modeling, SQL (Structured Query Language), Sales Support, Scorecarding, Set Goals, Software as a Service (SaaS), Statistics, Storytelling, Streaming Technology, Structured Analysis, Surface Modeling, Tableau, Testing, Usability Engineering, Workflow Analysis, ZenDesk, eCommerce ## Description * Building trusted datasets, definitions, and reporting that teams can rely on. * Turning ambiguous questions into structured analyses and measurable hypotheses. * Creating operational metrics and dashboards that are easy to use and drive action. * Applying AI thoughtfully, with clear success metrics and appropriate governance. You will * Own Customer Success and Customer Operations measurement * 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. * 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 that support day-to-day execution and executive visibility. * 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, such as drops in engagement, support spikes, onboarding delays, and renewal risk. * 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 for our \BPO partner * 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. * AI-enabled automation and productivity * Identify and prototype AI-driven workflows that reduce manual analysis and speed up decision-making, such as automated insights, narrative summaries, anomaly detection triage, and stakeholder Q&A. * Define success metrics and guardrails for AI-supported analytics, including accuracy, coverage, bias considerations, data privacy, and appropriate human review. * Drive adoption through enablement, feedback loops, and iteration with cross-functional partners. * Cross-functional partnership and storytelling * 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. * Build strong relationships with CS, Customer Ops, RevOps, Finance, Support, and Data teams to align priorities and execute effectively. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [The AI Agent Path to Prod: Building for Reliability](https://www.wearedevelopers.com/videos/1523-the-ai-agent-path-to-prod-building-for-reliability) - [JavaScript? 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