Lead Product Manager, Scribd

• 8+ years of product management experience, including 4+ years leading recommendations or search products in a high-traffic consumer environment
• Demonstrated success shipping ML-driven features that moved core business metrics (engagement, conversion, or revenue) at scale
• 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 and presenting data-backed decisions to senior leadership
• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience)

Desirable

• In this role, you'll own Scribd's recommendations experience — helping 200 million monthly visitors discover content they didn't know they were looking for, across a corpus of 300 million documents. You'll work at the intersection of ML and product to surface the right content to the right person, at the right time
• Success means defining a compelling vision, crafting metrics that truly matter, and experimenting your way to breakthrough features that redefine discovery — in close partnership with Engineering, Analytics, Data Science, Design, and Machine Learning teams
• Chart the long-term recommendations strategy — own a multi-year roadmap spanning candidate generation, ranking, and results presentation across Scribd's surfaces, guiding every user from interest to the right document
• Partner with ML Engineering & Applied Research — translate cutting-edge retrieval and ranking research into production systems that blend collaborative signals, content embeddings, and real-time behavioral data for best-in-class personalization
• Define the metrics that matter — establish and monitor leading indicators of recommendations success: engagement rate, click-through, content completion, and downstream subscription conversion and retention
• Balance short-term wins with long-term vision — ship incremental relevance improvements that hit revenue goals while building an extensible recommendations platform aligned with Scribd's 3-year AI strategy
• Fuse data with the voice of the customer — synthesize experiment results, behavioral analytics, user interviews, and feedback 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