Scribd

Scribd

Senior Paid Social Manager

San Francisco · Senior

Sponsorship not specifiedDetected 84 days ago
Data ScienceData VisualizationA/B TestingStakeholder ManagementMeta AdsPerformance MarketingCollaboration

About the role

  • We're seeking an experienced Senior Paid Social Manager to join our performance marketing team, focusing on scaling our presence across Meta, TikTok, Reddit, and exploring other emerging social platforms.
  • This role will be instrumental in driving user acquisition for our Everand and Fable products through social advertising strategies.

Responsibilities

  • Own and optimize paid social advertising budgets across Meta, TikTok, Reddit, and other social platforms for web and mobile app campaigns
  • Develop and execute paid social strategies aligned with our growth objectives and brand guidelines
  • Build and maintain sophisticated audience targeting frameworks, sustaining results through platform shifts and creative fatigue
  • Design and execute structured testing plans across bidding models, creative formats, and audience strategies
  • Collaborate with Creative to develop high-performing ad content, influencing briefs with performance data
  • Partner with Product and Engineering to identify landing page friction points, propose A/B test hypotheses, and own a CRO testing roadmap across landing pages
  • Partner with data science to develop attribution models, incrementality tests, and LTV signaling pipelines
  • Own regular analytics and reporting
  • Design and execute structured testing plans across bidding models, creative formats, and audience strategies; produce durable test plans and playbooks the team can reuse
  • Own regular analytics and reporting; effectively communicate performance, wins, and challenges to stakeholders

Requirements

  • 6+ years of experience managing paid social campaigns, with at least 3 years focused on subscription or D2C products
  • Proven track record of scaling social advertising spend efficiently while maintaining or improving LTV:CAC
  • Deep expertise in Meta Ads Manager, TikTok Ads, and Reddit Ads platforms, including structured testing across bidding, creative, and audience
  • Strong understanding of conversion optimization, audience targeting, and creative testing methodologies
  • Experience with attribution models and incrementality testing
  • Excellent analytical skills with proficiency in data visualization tools
  • Track record of cross-functional collaboration and stakeholder management
  • Our four products - Scribd®, Slideshare®, Everand™, and Fable - help billions of people across the globe move beyond access and into insight, application, and expertise.

Nice to have

  • Experience marketing subscription products or digital content services
  • Google Tag Manager experience
  • Familiarity with mobile app marketing and app store optimization
  • Familiarity and eagerness to work with AI tools
  • San Francisco is our highest geographic market in the United States.
  • We carefully consider a wide range of factors when determining compensation, including but not limited to experience
  • job-related skill sets
  • and other business and organizational needs.

Skills

  • This posting reflects an approved, open position within the organization.

Compensation

  • At Scribd, Inc., your base pay is one part of your total compensation package and is determined within a range.

Benefits

  • 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
  • Lead learning reviews and document test results so insights are actionable and built into future planning cycles
  • Scribd Flex (flexible work model)

This listing is sourced directly from Scribd's careers page and normalized into a canonical job model.