VP, Data & Analytics

Scribd•San Francisco, CA

2d•Hybrid

About The Position

Data & Analytics at Scribd, Inc. is the connective tissue between strategy and execution. The team works across all product, technology, marketing, and content functions to optimize the business of our four-brand portfolio. It spans three disciplines — Product Analytics, Data Science, and Analytics Engineering — and is responsible for measurement, experimentation, causal inference, predictive modeling, and the data infrastructure that makes all of it possible. We are investing in this function in a meaningful way. The opportunity is to take a strong foundation — a talented team, rich data assets, and a product portfolio that reaches hundreds of millions of people — and build the operating model, partnerships, and infrastructure that let D&A operate as a true strategic partner to the business. This is also a moment of transformation for the craft itself. Scribd runs a multi-brand portfolio where user behavior is shifting quickly — especially as AI reshapes how people discover and consume content — and the cost of slow, low-confidence decisions is rising. At the same time, AI-native tools — from agentic coding environments like Claude Code to AI-assisted analysis and automated reporting — are fundamentally changing what a high-performing analytics organization can accomplish. The convergence of these two forces makes this a unique opportunity: build the decision and measurement backbone that helps the company move faster with confidence across all four brands, using tools that multiply what every analyst can deliver. We are looking for a leader who doesn't just understand this shift but is eager to drive it. This role reports into the Chief Product and Technology Officer and manages director-level leaders across Product Analytics, Data Science, and Analytics Engineering.

Requirements

15+ years in analytics, data science, or a related discipline, with 5+ years leading a D&A organization at the VP level or equivalent — managing directors and their teams.
A track record of building high-performing D&A organizations that are valued as strategic partners, not service desks. You've done the work of aligning operating models, building cross-functional trust, and raising the bar on what analytics delivers.
Experience in high-velocity product environments where analytics supports product development, growth, and business optimization — not just reporting.
Fluency across both Product Analytics and Data Science. You don't need to be a hands-on practitioner, but you understand the boundaries of each discipline, when to deploy each, and how to evaluate the quality of the work.
Familiarity with analytics engineering fundamentals — data modeling, transformation frameworks (e.g., dbt), warehouse design, instrumentation standards, and data quality practices. You don't need to have been an analytics engineer, but you need to evaluate the work, set the strategy, and advocate for infrastructure investment.
Deep experience with experimentation: governance frameworks, self-serve enablement, and the organizational discipline to balance rigor with velocity.
A genuine enthusiasm for AI-native analytics. You are actively using tools like Claude Code, Copilot, or similar agentic coding environments and have a clear point of view on how they change what a D&A team can accomplish.
A bias toward speed and pragmatic sequencing. You hold a high bar without letting perfectionism slow the team down.
Cross-functional leadership. You align product, engineering, finance, and marketing around a shared data strategy through influence, not authority.
Executive-level communication. You tell a crisp, compelling story with data to any audience — from an analyst to a board member.
A track record of hiring, developing, and retaining exceptional talent — including leaders who go on to outgrow you.
Experience at a consumer subscription or media company — familiarity with LTV, retention, churn, and subscription economics as the primary lens for business performance.

Nice To Haves

Hands-on background in causal inference, incrementally measurement, or LTV/churn modeling — enough to know when a methodology is sound, even if you're not running the analysis yourself.
Experience scaling reporting infrastructure — building self-serve BI, standardizing KPI definitions, and earning organizational trust in the numbers.
Familiarity with modern analytics infrastructure: experimentation platforms, cloud data warehouses, BI tools, and transformation frameworks.

Responsibilities

Evolve how D&A partners with every function in the company — product, engineering, marketing, content, and finance — with clear ownership, shared expectations, and a model that allocates resources where they create the most leverage.
Refine the experimentation governance framework — enabling teams to move fast on routine decisions while maintaining rigorous oversight on high-stakes bets.
Own the company's data governance strategy — including data quality standards, documentation, privacy practices, and literacy programs — in partnership with Legal and Engineering.
Establish D&A as the long-term steward of how the company collects, shares, uses, and trusts its data.
Lead and develop director-level leaders across Product Analytics, Data Science, and Analytics Engineering.
Raise the talent bar. Recruit exceptional people, invest in their growth, and build a team with the depth and density to operate at speed without single points of failure.
Foster a culture of ownership, intellectual curiosity, and high standards — where the team is empowered to make great decisions without needing you in the room.
Drive the adoption of agentic coding tools (like Claude Code), AI-assisted analysis, and automated reporting workflows across the D&A organization.
Reimagine what a D&A team can accomplish when routine analysis, data cleaning, and reporting are accelerated by AI — and redirect that capacity toward higher-value strategic work.
Build the playbooks, training, and cultural norms that make AI-native workflows the default, not the exception.
Lead opportunity sizing and strategic planning that gives business leaders data-backed confidence in their biggest decisions.
Shorten the cycle from question to insight.
Build self-serve capabilities that raise data literacy across the company and make standard reporting a business-owned activity.
Drive the data infrastructure strategy — certified datasets, experimentation platform maturity, and instrumentation standards — so analysts spend their time generating insight, not debugging pipelines.
Serve as the data voice in the C-suite — translating analytical findings into clear, actionable recommendations for senior leadership and the board.
Make the company's learning agenda visible: what we're testing, what we're learning, and what it means for growth, retention, and long-term value.
Partner across the portfolio to embed data into strategic decisions — before they're made, not after.

Benefits

Scribd Flex (flexible work model)
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
Complimentary access to the Scribd, Inc. suite of products
Enterprise access to leading AI tools