Our four products – Scribd®, Slideshare®, Everand™, and Fable – help billions of people across the globe move beyond access and into insight, application, and expertise. and where every employee is empowered to take action as we prioritize the customer. We believe the best work happens when individual flexibility is balanced with meaningful community connection. Scribd Flex empowers employees to choose the workstyle and location that support their best performance, while committing to intentional in-person moments that strengthen collaboration and culture. Traditionally defined as the intersection of passion and perseverance toward long-term goals, GRIT reflects the mindset we expect from every employee. For us, it also serves as a practical framework for how we work: setting and achieving Goals, delivering Results within your role, contributing Creative ideas and solutions, and strengthening the broader Team through collaboration and attitude. 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. 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. 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. 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. 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. When a metric moves sharply, your instinct is to find the field or pipeline that changed before you write the narrative. Build measurement for content understanding. 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. 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. 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. A/B test design, hypothesis testing, and statistical analysis. 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

📌 Lead Product Analyst (Toronto)
🏢 Scribd
📍 Toronto