Lead Product Analyst (Content Supply & Understanding)
Scribd
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Benefits offered by this job
Job summary
Scribd is seeking Senior or Lead Data Analysts to own analytics for content supply and understanding across a library of 400M+ documents and 250M+ MAU. You will define KPIs, run A/B tests, and build AI-assisted analytics to inform product decisions.
You’ll partner with Product, Engineering and Data teams to size opportunities, model ROI, and ensure data quality as content sources and pipelines evolve.
Qualifications
- Experience with opportunity sizing, segmentation, or business and go-to-market analysis.
- Experience turning ambiguous questions into analysis that changed decisions.
- Experience with a modern data warehouse such as Databricks and BI tools (Looker/Tableau).
- Strong written and verbal communication for technical and non-technical audiences.
- Experience partnering with Product Managers and Engineers on strategy, roadmap, and measurement.
- 4+ years in product analytics (Senior) or 7+ years (Lead) with impact on product decisions.
- Experience building AI into analytics workflows (LLM-assisted querying/reporting).
- Advanced SQL and experience with Python or R for analysis.
Responsibilities
- Own analytics for content supply and generate insights to close gaps between demand and corpus.
- Define KPIs, metric definitions, and dashboards for the content domain.
- Design and read experiments (A/B tests) for content-related product changes.
- Partner with Product Managers on strategy and roadmap; translate bets into success metrics.
- Present findings to product, engineering, and executive audiences.
Skills
Education
Tools
Job description
- The team. Data & Analytics at Scribd brings together Analytics Engineering, Data Science, and Product Analytics to turn fragmented data and inconsistent definitions into trusted foundations, and to build AI-assisted tools that bring reliable intelligence into how teams make decisions
- The role. This role supports Content Supply & Understanding, the team that owns Scribd’s library of more than 400 million documents, which reaches over 250 million monthly active users. The team’s remit covers who contributes the content, how well we understand it, and how it reaches users
- You will report to the Senior Manager, Data Analytics, and work embedded alongside Product, Engineering, Machine Learning, Data Science, and Analytics Engineering partners. The role is open at the Senior and Lead levels, and we will level based on the experience you bring
- At the Senior level you own analytical workstreams within a defined area. At the Lead level you also set the measurement strategy for the area, take on the most open-ended problems, and raise the bar for the analysts around you
- Mandate. Make it possible to say with confidence what is in the corpus, where it falls short of what users look for, and whether enrichment is making content easier to find and use, with numbers that stay comparable as content sources, models, and pipelines change
- Core philosophy. A metric definition is a shared artifact. We care about documentation, versioning, and data quality as much as the readout on top, because a number that quietly breaks upstream is worse than no number at all
- Build measurement for content understanding. Define how we measure classification and metadata coverage, accuracy, and the effect of enrichment on discovery and engagement
- Own analytics for content supply and generate insights on where the corpus falls short: size the gap between user demand and the corpus, estimate the value of closing it, and help prioritize where to invest, using the same market sizing, segmentation, and ROI methods applied in go-to-market analysis
- Partner with Product Managers on strategy and roadmap. Translate product bets into success and guardrail metrics, size opportunities, and make trade-offs explicit
- Design and read experiments (A/B tests and other methods) for content-related product changes, from hypothesis through analysis and readout
- Define and maintain the KPIs, metric definitions, and dashboards for the content domain, and work with Analytics Engineering and Data Engineering on instrumentation and data quality so that metrics stay consistent over time
- Investigate discontinuities and anomalies in content data, and separate real changes in user behavior from pipeline, definition, or instrumentation changes
- Use AI-assisted workflows to speed up analysis and reporting, document how results are validated, and keep AI tools grounded as definitions change
- Present findings and recommendations to product, engineering, and executive audiences, including in regular metrics reviews
- At the Lead level: set analytical standards for the area and mentor other analysts
- Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees
- 12 weeks paid parental leave
- Short-term/long-term disability plans
- 401k/RSP matching
- Tuition Reimbursement
- Learning & Development programs
- Quarterly stipend for Wellness, Connectivity & Comfort
- Mental Health support & resources
- Free subscription to Scribd + gift memberships for friends & family
- Referral Bonuses
- Book Benefit
- Sabbaticals
- Company wide events
- Team engagement budgets
- Vacation & Personal Days
- Paid Holidays (+ winter break)
- Flexible Sick Time
- Volunteer Day
- Company-wide Diversity, Equity, & Inclusion programs
You are at your best when the question is not yet well formed. You look at an incomplete picture, work out what matters, and produce an answer a product team can act on, with the limits of the evidence stated plainly
You are skeptical by default. When a metric moves sharply, your instinct is to find the field or pipeline that changed before you write the narrative
You communicate early and plainly, and you write things down. Methodology and metric documentation are part of the deliverable
You are equally comfortable as the strategist and the analyst. You can size an opportunity, model its return, write the SQL behind the metric, and defend the number in a room full of product and executive leaders
You are pragmatic. You ask what the right next version is, and you use AI where it creates measurable value
Experience with opportunity sizing, segmentation, or business and go-to-market analysis, such as market sizing, pipeline prioritization, or ROI modeling
Experience turning ambiguous, loosely defined questions into analysis that changed a decision, in areas where data is sparse or metrics did not yet exist
Experience with a modern data warehouse such as Databricks, and with BI tools such as Looker or Tableau
Strong written and verbal communication, for both technical and non-technical audiences
Bachelor’s degree in a quantitative field such as Statistics, Mathematics, Economics, or Computer Science, or equivalent practical experience
Working knowledge of experimentation: A/B test design, hypothesis testing, and statistical analysis
Experience partnering with Product Managers and Engineers on strategy, roadmap, and measurement
4+ years of experience in product analytics or a closely related role for Senior, or 7+ years for Lead, with a record of influencing product or business decisions
Experience building AI into analytics workflows, such as LLM-assisted querying, analysis, or reporting, to move faster while checking answers against certified metrics or known results. You use AI where it creates measurable value
A record of defining a metric, defending it under scrutiny, and revising it when its inputs change
Advanced SQL, and experience with Python or R for analysis and statistical work
Experience with content-heavy platforms such as marketplaces, catalogs, publishing, or user-generated content, including supply or content quality analytics
Experience with classification, taxonomy, or metadata systems, or with search and recommendation, including evaluating ML or LLM outputs against labeled data
Experience with experimentation platforms such as Statsig
For Lead candidates: experience mentoring analysts and setting measurement standards for a product area
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