Lead Product Manager (Recommendations)

Scribd; Dallas (TX); On-site; USD 140,000 - 180,000; Full time

Benefits offered by this job

Healthcare Insurance Coverage (Medical

12 weeks parental leave

401k matching

Tuition Reimbursement

Sabbaticals

Company-wide events

Wellness stipend

Job summary

Scribd seeks a senior product manager to own the recommendations experience for hundreds of millions of readers. You’ll shape the end-to-end discovery journey, balancing retrieval, ranking, and personalization with a scalable platform vision.

You’ll partner with ML, data science, design and engineering to define metrics, drive multi-year roadmaps, and ship ML-driven features that move engagement and revenue while guiding Scribd’s 3-year AI strategy.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience).
• Exceptional written and verbal communication skills — adept at crafting product briefs and presenting data-backed decisions to senior leadership.
• 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.
• 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.
• Track record of thriving amid ambiguity: shaping a multi-year vision, aligning cross-functional teams, and delivering incremental wins along the way.
• 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

• 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.
• Define a compelling vision, craft metrics that truly matter, and experiment your way to breakthrough features that redefine discovery in partnership with Engineering, Analytics, Data Science, Design, and ML teams.
• Chart the long-term recommendations strategy — own a multi-year roadmap spanning candidate generation, ranking, and results presentation across Scribd surfaces.
• Partner with ML Engineering & Applied Research — translate cutting-edge retrieval and ranking research into production systems blending signals, embeddings, and real-time data.
• Define the metrics that matter — establish and monitor leading indicators of recommendations success: engagement rate, click-through, content completion, and subscription metrics.
• Balance short-term wins with long-term vision — ship incremental relevance improvements that hit revenue goals while building an extensible platform.
• Fuse data with the voice of the customer — synthesize experiment results and feedback to inform prioritization and design.
• Communicate with clarity and influence — align product, engineering, design, content, and executives on requirements, timelines, deliverables, and impact.

Skills

Product management

Retrieval algorithms

Ranking algorithms

Embeddings

Feature stores

LLM-powered workflows

SQL copilots

Data exploration tools

GenAI‑enhanced discovery

ML ops tooling

Education

Bachelor’s degree in CS/Engineering/Math

Tools

SQL copilots

Data exploration tools

ML ops tooling

Job description

Responsibilities

• 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

Benefits

• Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees
• 12 weeks paid parental leave
• Short‑term/long‑term disability plans
• 401k/RSP matching
• Tuition Reimbursement
• Learning & Development programs
• Quarterly stipend for Wellness, Connectivity & Comfort
• Mental Health support & resources
• Free subscription to Scribd + gift memberships for friends & family
• 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 Diversity, Equity, & Inclusion programs

Requirements

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field (or equivalent practical experience)
• Exceptional written and verbal communication skills — adept at crafting product briefs and presenting data‑backed decisions to senior leadership
• 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
• 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
• Track record of thriving amid ambiguity: shaping a multi‑year vision, aligning cross‑functional teams, and delivering incremental wins along the way
• 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