Who you are

- 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)

- 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

What the job involves

- 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)

- Adaptable Sick Time

- Volunteer Day
- Company-wide Diversity, Equity, &

- Inclusion programs

📌 Lead Product Manager (Toronto)
🏢 Scribd
📍 Toronto