Lead Product Manager

About The Company At Scribd (pronounced "scribbed"), our mission is to spark human curiosity. Join our team as we create a world of stories and knowledge, democratize the exchange of ideas and information, and empower collective expertise through our three products : Everand, Scribd, and Slideshare. We support a culture where our employees can be real and be bold; where we debate and commit as we embrace plot twists; and where every employee is empowered to take action as we prioritize the customer. When it comes to workplace structure, we believe in balancing individual flexibility and community connections. It's through our flexible work benefit, Scribd Flex, that employees - in partnership with their manager - can choose the daily work-style that best suits their individual needs. A key tenet of Scribd Flex is our prioritization of intentional in-person moments to build collaboration, culture, and connection. For this reason, occasional in-person attendance is required for all Scribd employees, regardless of their location. So what are we looking for in new team members? Well, we hire for "GRIT". The textbook definition of GRIT is demonstrating the intersection of passion and perseverance towards long term goals. At Scribd, we are inspired by the potential that this can unlock, and ask each of our employees to pursue a GRIT-ty approach to their work. In a tactical sense, GRIT is also a handy acronym that outlines the standards we hold ourselves and each other to. Here's what that means for you : we're looking for someone who showcases the ability to set and achieve oals, achieve esults within their job responsibilities, contribute nnovative ideas and solutions, and positively influence the broader eam through collaboration and attitude. About the team & role We're looking for a Lead Product Manager to set the strategy and drive execution for Scribd's Recommendations & Personalization team. In this pivotal role, you'll transform our early-stage recommender work into a real-time personalization platform that serves 200 million monthly visitors , delivers 1.2 billion recommendation impressions every month , and guides exploration across a corpus of 300 million documents Leveraging advanced personalization algorithms, LLM-powered semantic insights, and fine-grained behavioral signals, you'll deliver highly relevant research paths and content that help students, educators, and professionals tackle high-stakes tasks with confidence. You will / Responsibilities Chart the long-term recommendation & personalization strategy — own a multi-year roadmap that spans the Scribd homepage, our content graph, and yet to be defined product surfaces, guiding every user to their next must-read document or knowledge insight. Partner with Machine-Learning Engineering & Applied Research — translate cutting-edge retrieval and ranking research into production algorithms that blend semantic embeddings, and real-time behavior for best-in-class relevance and trust. Define the metrics that matter — establish and monitor the leading indicators of subscription conversion, retention, and lifetime value, using them both to evaluate product success and to feed continuous model-training loops. Balance short-term wins with long-term vision — deliver incremental improvements that hit our revenue goals while building an extensible personalization platform aligned with Scribd's 3-year AI strategy. Fuse data with the voice of the customer — synthesize experiment results, usage analytics, user interviews, and feedback sessions to inform prioritization and feature design. Communicate with clarity and influence — align product, engineering, design, content, and executive stakeholders by clearly articulating requirements, timelines, deliverables, and expected impact for critical initiatives, securing buy-in at every stage. You have / Requirements 7+ years of product management experience , including 4+ years leading recommender systems, personalization, or large-scale search / ranking products in a high-traffic consumer environment. Demonstrated success shipping ML-driven recommendation features that moved core business metrics (engagement, conversion, or revenue) for large scale products. Deep familiarity with retrieval and ranking algorithms, embeddings, and feature stores — paired with the ability to reason about end-to-end customer journeys for distinct user segments. Track record of thriving amid ambiguity : shaping a multi-year vision, aligning cross-functional teams, and delivering incremental wins along the way. Exceptional written and verbal communication skills — adept at crafting product briefs , bringing in inputs from multiple teams, and presenting data-backed decisions to senior leadership Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field (or equiv #J-18808-Ljbffr