Lead Product Manager - Scribd Recommendations and Personalization

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 Goals, achieve Results within their job responsibilities, contribute Innovative ideas and solutions, and positively influence the broader Team 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.

Success here means defining a compelling vision, crafting metrics that truly matter, and experimenting your way to breakthrough features that redefine personalization and engagement—all in close partnership with Engineering, Analytics, Data Science, Design, and Machine Learning teams who turn strategy into production reality. The ideal candidate combines deep technical fluency in ML/AI with seasoned product management chops and a track record of translating data-driven insights into meaningful product wins. Your blend of long-range thinking and hands-on execution will shape the future of how the world discovers and synthesizes knowledge on Scribd.

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 equivalent practical experience).
  

Ideally, you also / Bonus points

• Hands-on proficiency with SQL and a scripting language (Python or R) for rapid data exploration, prototyping, and model evaluation.

• Experience designing LLM- and GenAI-enhanced user experiences that go beyond raw chat to deliver task-specific value.

• Familiarity with modern ML operations tooling—from vector databases to end-to-end model-ops workflows (MLOps / LLMOps)—and the ability to collaborate with engineering teams that rely on these systems.

• Passion for the written word and open knowledge—a genuine drive to build products that democratize expertise and help people learn, teach, and create.

At Scribd, your base pay is one part of your total compensation package and is determined within a range. Our pay ranges are based on the local cost of labor benchmarks for each specific role, level, and geographic location. San Francisco is our highest geographic market in the United States. In the state of California, the reasonably expected salary range is between $157,500 [minimum salary in our lowest geographic market within California] to $246,000 [maximum salary in our highest geographic market within California].

In the United States, outside of California, the reasonably expected salary range is between $130,000 [minimum salary in our lowest US geographic market outside of California] to $233,500 [maximum salary in our highest US geographic market outside of California].

In Canada, the reasonably expected salary range is between $165,000 CAD[minimum salary in our lowest geographic market] to $219,000 CAD[maximum salary in our highest geographic market].

We carefully consider a wide range of factors when determining compensation, including but not limited to experience; job-related skill sets; relevant education or training; and other business and organizational needs. The salary range listed is for the level at which this job has been scoped. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for a competitive equity ownership, and a comprehensive and generous benefits package.

Working at Scribd, inc.

Are you currently based in a location where Scribd is able to employ you?
Employees must have their primary residence in or near one of the following cities. This includes surrounding metro areas or locations within a typical commuting distance:

United States:

Atlanta | Austin | Boston | Dallas | Denver | Chicago | Houston | Jacksonville | Los Angeles | Miami | New York City | Phoenix | Portland | Sacramento | Salt Lake City | San Diego | San Francisco | Seattle | Washington D.C.

Canada:

Ottawa | Toronto | Vancouver

Mexico:

Mexico City

Benefits, Perks, and Wellbeing at Scribd

*Benefits/perks listed may vary depending on the nature of your employment with Scribd and the geographical location where you work.

• Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees

• 12 weeks paid parental leave

• Short-term/long-term disability plans

• 401k/RSP matching

• Onboarding stipend for home office peripherals + accessories

• Learning & Development allowance

• Learning & Development programs

• Quarterly stipend for Wellness, WiFi, etc.

• Mental Health support & resources

• Free subscription to the Scribd Inc suite of products

• Referral Bonuses

• Book Benefit

• Sabbaticals

• Company-wide events

• Team engagement budgets

• Vacation & Personal Days

• Paid Holidays (+ winter break)

• Flexible Sick Time

• Volunteer Day

• Company-wide Employee Resource Groups and programs that foster an inclusive and diverse workplace.

• Access to AI Tools: We provide free access to best-in-class AI tools, empowering you to boost productivity, streamline workflows, and accelerate bold innovation.

Want to learn more about life at Scribd? www.linkedin.com/company/scribd/life

We want our interview process to be accessible to everyone. You can inform us of any reasonable adjustments we can make to better accommodate your needs by emailing accommodations@scribd.com about the need for adjustments at any point in the interview process.

Scribd is committed to equal employment opportunity regardless of race, color, religion, national origin, gender, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law. We encourage people of all backgrounds to apply, and believe that a diversity of perspectives and experiences create a foundation for the best ideas. Come join us in building something meaningful.