Lead Product Manager, Recommendations

Scribd•San Francisco, CA

3d•Hybrid

About The Position

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.

Requirements

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)

Nice To Haves

Hands-on proficiency with AI tools for productivity and analytics — including LLM-powered workflows, SQL copilots, and data exploration tools — to move fast, prototype ideas, and pressure-test assumptions without always needing engineering support
Experience designing LLM- and GenAI-enhanced discovery experiences that go beyond raw recommendations to deliver personalized, task-specific value
Familiarity with modern ML ops tooling

Responsibilities

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

Benefits

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