Sr Data Analyst, Customer Operations

Scribd, Inc.•Vancouver, BC

1d•$80,000 - $146,000•Hybrid

About The Position

Scribd is evolving from one of the world’s largest document libraries into a platform where active knowledge building happens. With a library of 300 million documents and over 250 million monthly visitors, our next chapter focuses on helping users move from simply finding information to building deep, actionable understanding. As the Senior Analytics Lead you will own analytics and operational insights for the Customer Operations organization. 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.

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.
Strong SQL skills and experience working with analytical datasets and BI tools (Looker, Tableau, etc.), with an emphasis on performance, usability, and metric governance.
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 Haves

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.
Familiarity with tools such as Forethought (AI) and Zendesk

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.

Benefits

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
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