Senior Data Analyst, Customer Operations

Scribd, Inc.; Vancouver; Hybrid; CAD 101,500 - 129,500; Full time

Benefits offered by this job

Health, dental, and vision insurance

Paid time off

Retirement matching

Learning and development opportunities

Job summary

Scribd, Inc. is seeking a Senior Data Analyst based in Vancouver to lead the Customer Success Analytics function. The role involves defining metrics, enhancing reporting capabilities, and collaborating across teams to optimize customer operations.

The ideal candidate will have 4+ years in analytics, proficient in SQL and Python, and have experience in customer-facing functions, along with strong communication skills to relay insights effectively.

Qualifications

• Experience in business operations or business intelligence roles supporting customer-facing functions.
• Familiarity with subscription metrics like LTV, churn, and renewal rates.
• Strong analytical skills and comfort with BI tools.

Responsibilities

• Own Customer Success and Operations measurement.
• Build and iterate on dashboards and operational reporting.
• Define retention metrics and operationalize churn analyses.

Skills

SQL

Python

Data analysis

Communication skills

Education

4+ years of experience in analytics

Tools

Looker

Tableau

Databricks

Zendesk

Job description

About the Role

As the first Senior Data Analyst embedded in Customer Operations, you will build the CS Analytics function and own analytics and operational insights for the department. You will partner closely with Customer Operations leadership, 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.

Responsibilities

• 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.

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 with 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 collaborating 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.
• Strong foundation in statistics and experimental thinking, including hypothesis testing and measurement design.
• Strong communication skills, with 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 (e.g., 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.

Compensation and Benefits

• Base salary range: $97,000 – $146,000 for California (San Francisco), $80,000 – $138,500 for other US locations, $101,500 – $129,500 CAD for Canada.
• Competititive equity ownership.
• Comprehensive benefits including health, dental, vision, disability, paid time off, parental leave, retirement matching, learning and development, and wellness stipends.
• Access to Scribd services and enterprise AI tools.

Equal Employment Opportunity Statement

Scribd, Inc. is committed to equal employment opportunity regardless of race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law. We encourage people of all backgrounds to apply.