Lead Product Analyst, Content Supply & Understanding

• 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 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 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 are pragmatic. You ask what the right next version is, and you use AI where it creates measurable value.

• You communicate early and plainly, and you write things down. Methodology and metric documentation are part of the deliverable.

• 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.

• 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 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 opportunity sizing, segmentation, or business and go-to-market analysis, such as market sizing, pipeline prioritization, or ROI modeling.

• Advanced SQL, and experience with Python or R for analysis and statistical work.

• Experience with a modern data warehouse such as Databricks, and with BI tools such as Looker or Tableau.

• 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.

• Working knowledge of experimentation: A/B test design, hypothesis testing, and statistical analysis.

• A record of defining a metric, defending it under scrutiny, and revising it when its inputs change.

• Experience partnering with Product Managers and Engineers on strategy, roadmap, and measurement.

• 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.

• 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.

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. San Francisco is our highest geographic market in the United States.

In the state of California, the reasonably expected salary range is between $153,000 [minimum salary in our lowest geographic market within California] to $216,500 [maximum salary in our highest geographic market within California].

In the United States, outside of California, the reasonably expected salary range is between $127,000 [minimum salary in our lowest US geographic market outside of California] to $205,500 [maximum salary in our highest US geographic market outside of California].

In Canada, the reasonably expected salary range is between $152,000 CAD[minimum salary in our lowest geographic market] to $ 194,000[maximum salary in our highest geographic market].

We carefully consider a wide range of factors when determining compensation, including but not limited to experience; job-related skill sets; relevant education or training; and other business and organizational needs. The salary range listed is for the level at which this job has been scoped. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for a competitive equity ownership, and a comprehensive and generous benefits package. 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. San Francisco is our highest geographic market in the United States.

• 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