Lead Data Scientist - Recommendations

About The Company:

At Scribd Inc. (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 four products:
Everand, Scribd, Slideshare, and Fable.

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

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 Inc. employees, regardless of their location.

So what are we looking for in new team members? We hire for “GRIT”. The textbook definition of GRIT is demonstrating the intersection of passion and perseverance towards long term goals. At Scribd Inc., 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.

Scribd’s Data & Analytics team is hiring a Lead Data Scientist to own measurable outcomes across our recommendation surfaces – translating product goals into metrics, leading roadmap bets, and shipping lifts in business results. You’ll define the offline/online contract end-to-end, design and run experiments, diagnose why variants win or lose, and build prototype models while partnering with Engineering to product ionize. You’ll map goals to metrics with clear success criteria, focus on opportunity sizing and measurement, and apply an AI lens (LLMs, embeddings) where it demonstrably improves retrieval, ranking, or understanding—shaping how millions engage with our global content library.

Scribd is a differentiated subscription platform with strong organic reach and a vast catalog—books, audio books, and hundreds of millions of UGC documents and slides. In a landscape reshaped by AI, our opportunity is to help users cut through noise and discover high-quality, human-centered content. You’ll set north stars and guardrails, create leading indicators that predict long-term outcomes, and build the measurement architecture—identity, attribution windows, metric contracts, and drift/leakage checks—that keeps downstream metrics trustworthy.

You’ll also accelerate decision velocity with clear stop/go criteria and power checks, and tell the story through concise decision memos with trade-offs and risks.

• Opportunity mapping. Size and prioritize new recs surfaces, intents, and cohorts; trace the funnel and analyze by slice (cold items, long-tail users, platform) to steer the roadmap.
• Own the evaluation framework. Define north star & guardrails (e.g. diversity, novelty, duplication, safety); set threshold and tradeoffs, and publish the Objective & Eval Contract per surface.
• Offline/Online alignment. Quantify correlation between offline IR metrics (e.g., NDCG@K, MAP, MRR, coverage, calibration) and online KPIs by surface/cohort; publish error bounds and monitor metric drift.
• Create leading indicators. Create short-horizon metrics that predict long-term outcomes (e.g., trial to bill-through); backtest and run post-hoc causal checks, reporting uncertainty.
• Build the measurement architecture. Set identity & attribution standards ( vs. , qualifying events, windows)…