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, enabling self-serve analytics, and developing forecasting models.

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.
• Build strong relationships with CS, Customer Ops, RevOps, Finance, Support, and Data teams to align priorities and execute effectively.

Requirements

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.

Qualifications

• 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.

Skills

• Deep analytical thinking and problem-solving skills.
• Ability to operate in a fast-paced, evolving environment.
• Technical skills in SQL, Python, and BI tools.
• Experience with Databricks and data engineering practices.
• Strong communication and relationship-building skills.
• Comfort with ambiguity and the ability to translate complex questions into clear measurements.
• Experience with AI and LLM-enabled analytics workflows.

Benefits

At Scribd, Inc., you will receive:

• Scribd Flex (flexible work model)
• Comprehensive health, dental, and vision coverage
• Mental health support and disability coverage
• Generous paid time off, including vacation, sick time, holidays, winter break, volunteer time, and sabbaticals
• Paid parental leave and family support benefits
• Retail retirement matching and employee equity
• Learning and development programs and professional growth opportunities
• Wellness and home office stipends
• Complimentary access to the Scribd, Inc. suite of products
• Enterprise access to leading AI tools

Pay

The reasonably expected salary range for this position is between $80,000 and $138,500 USD, depending on the geographic location.

Schedule

Occasional in-person attendance is required for all Scribd, Inc. employees, regardless of location.