Lead Product Manager, Recommendations

About the role

This role is dedicated to enhancing content discovery for Scribd's 200 million monthly users. As a Lead Product Manager, you will be responsible for guiding the recommendations experience to help users find relevant content efficiently from a vast library of 300 million documents. Your work will involve applying machine learning principles to improve the relevance and timing of content delivery, ensuring users receive personalized and engaging recommendations. This position requires strategic thinking, cross-functional collaboration, and a deep understanding of recommendation systems and user behavior to support Scribd's growth, engagement, and revenue objectives.

Key facts

What you'll do

• Develop a comprehensive, long-term strategy for recommendations, including creating a multi-year roadmap that covers content generation, ranking, and display across all Scribd platforms.
• Collaborate closely with ML Engineering and Applied Research teams to implement advanced retrieval and ranking techniques, integrating collaborative data, content embeddings, and real-time user behavior signals to improve recommendation relevance.
• Establish, monitor, and analyze key performance indicators such as engagement metrics, click-through rates, content completion rates, and subscription conversion and retention to measure the success of recommendation features.
• Balance immediate improvements in relevance and user experience that meet revenue targets with the development of a scalable, robust recommendations platform aligned with Scribd's three-year AI plan.
• Use data from experiments, behavioral analytics, user interviews, and feedback to prioritize new features, optimize existing ones, and guide design decisions that enhance user satisfaction and engagement.
• Clearly communicate product requirements, project timelines, deliverables, and expected impacts to cross-functional teams including product managers, engineers, designers, content teams, and executive leadership.
• Lead initiatives to improve personalization and content discovery, leveraging machine learning models, user data, and content metadata to deliver tailored recommendations.
• Drive continuous improvement by analyzing system performance, user feedback, and industry trends to refine recommendation algorithms and user experience.
• Coordinate with content and engineering teams to ensure the recommendation system integrates seamlessly with other platform features and supports overall product goals.
• Advocate for best practices in data-driven decision-making, experimentation, and scalable system design to ensure the recommendations platform remains innovative and effective.
• Stay informed about emerging trends in AI, machine learning, and recommendation systems to incorporate cutting-edge techniques into Scribd's platform.

Requirements

• Minimum of 8 years of experience in product management, with at least 4 years leading recommendations or search products in high-volume consumer environments.
• Proven track record of launching machine learning-powered features that have significantly improved core business metrics such as engagement, conversion, or revenue at scale.
• Deep understanding of retrieval and ranking algorithms, embeddings, feature stores, and the ability to analyze and optimize customer journeys for different user segments.
• Experience navigating ambiguous situations, setting a multi-year vision, and coordinating diverse teams to achieve incremental progress toward strategic goals.
• Strong written and verbal communication skills, capable of preparing detailed product briefs, presenting data-supported decisions, and aligning stakeholders at all levels of the organization.
• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent practical experience.
• Demonstrated ability to work effectively in cross-functional teams, managing multiple priorities and delivering results in a fast-paced environment.
• Familiarity with data analysis, experimentation, and metrics-driven product development.

Nice to have

• Practical experience with AI tools for productivity and analytics, including large language model (LLM) workflows, SQL copilots, and data exploration tools for rapid prototyping and hypothesis testing.
• Experience designing discovery experiences enhanced by LLM and Generative AI, providing personalized, task-specific value beyond standard recommendations.
• Knowledge of modern ML operations (ML Ops) tools and practices to support scalable, reliable deployment of machine learning models.
• Understanding of content and user engagement metrics specific to digital content platforms.
• Exposure to enterprise AI tools and the ability to leverage them for product innovation and efficiency.

Skills & tools

• Machine Learning (ML)
• Retrieval and Ranking Algorithms
• Embeddings
• Feature Stores
• Behavioral Analytics
• SQL (copilots)
• Large Language Models (LLMs)
• Generative AI
• ML Ops tooling

Practical notes

This position offers competitive equity ownership and a comprehensive benefits package. Scribd Flex provides a flexible work model, but occasional in-person attendance is required at the San Francisco office. Benefits include health, dental, and vision coverage, mental health support, disability coverage, generous paid time off, parental leave, retirement matching, learning and development programs, wellness stipends, and access to Scribd products and enterprise AI tools. Employees must reside in or near approved cities in the US, including San Francisco, and in Canada (Ottawa, Toronto, Vancouver), or Mexico (Mexico City). If accommodations are needed during the interview process, please contact accommodations@scribd.com.

About the company

Explore Scribd, Inc., the home of Scribd, Slideshare, Everand, and Fable. Learn about our mission and vision.