Senior Data Analyst, Customer Operations

About the role

This role is embedded in Customer Operations and involves building the CS Analytics function. Key responsibilities include defining and maintaining core metrics and business definitions, creating operational metrics and dashboards, building decision-ready reporting, and enabling self-serve analytics.

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.
• Create clear documentation and enable consistent interpretation across Customer Success Operations, RevOps, Finance.
• 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.
• 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.
• 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.), 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.

Qualifications

• 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.

Skills

• Deeply analytical thinking and problem-solving skills.
• Ability to translate ambiguous questions into clear measurement frameworks, forecasting models, and actionable reporting.
• Strong communication and storytelling skills to influence stakeholders across technical and non-technical teams.
• Ability to work independently in a fast-paced environment with evolving priorities.

Benefits

At Scribd, Inc., your base pay is one part of your total compensation package and is determined within a range. Our pay ranges are based on the local cost of labor benchmarks for each specific role, level, and geographic location. Benefits include comprehensive health, dental, and vision coverage, mental health support and disability coverage, generous paid time off, retirement matching and employee equity, learning and development programs, and wellness and home office stipends. Additionally, there is enterprise access to leading AI tools and complimentary access to the Scribd, Inc. suite of products.