Job Details

Lead Data Scientist

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 productionize. 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 understandingshaping how millions engage with our global content library.

Scribd is a differentiated subscription platform with strong organic reach and a vast catalogbooks, audiobooks, 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 architectureidentity, attribution windows, metric contracts, and drift/leakage checksthat 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.

What you'll do:

• 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 (user_id vs. device_id, qualifying events, windows) so downstream metrics (bill-through, churn) are trustworthy.
• Design and run advanced experiments such as interleaving tests, pre-register stop/go criteria, and deliver crisp readouts that drive decisions.
• Codify schemas, freshness, leakage, and drift checks with Analytics and Data Engineers, establish high quality datasets for Recs algo.
• Evaluate when LLMs/embeddings (topics, summaries, semantic similarity) measurably improve offline/online metrics; prototype and hand off clear build specs to ML Eng.
• Storytelling and influence. Write decision memos, align cross-functional teams, and drive clear decisions with trade-offs and risks called out.

What you'll need:

• 8+ years experience in Data Science, preferably on recs/search/ranking with shipped impact.
• Strong Python and SQL; comfort with Spark.
• Fluency in ranking evaluation (NDCG@K, MAP, MRR, calibration, coverage/diversity) and awareness of exposure/selection bias.
• Fluency in experiment design and connecting offline metrics to online outcomes.
• Ability to translate product goals into loss functions, features, and specs engineers can build.

Nice to have:

• Familiarity with LLMs/embeddings evaluation in offline and online; embeddings/vector search assessment for lift vs. latency/cost

At Scribd, your base pay is one part of your total compensation package and is determined within a range. Our pay ranges are based on the local cost of labor benchmarks for each specific role, level, and geographic location. San Francisco is our highest geographic market in the United States. In the state of California, the reasonably expected salary range is between $162,000 to $252,500. In the United States, outside of California, the reasonably expected salary range is between $133,000 to $239,500. In Canada, the reasonably expected salary range is between $169,000 CAD to $224,500 CAD. We carefully consider a wide range of factors when determining compensation, including but not limited to experience; job-related skill sets; relevant education or training; and other business and organizational needs. The salary range listed is for the level at which this job has been scoped. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for a competitive equity ownership, and a comprehensive and generous benefits package.

Working at Scribd, Inc.

Are you currently based in a location where Scribd is able to employ you? Employees must have their primary residence in or near one of the following cities. This includes surrounding metro areas or locations within a typical commuting distance:

United States: Atlanta | Austin | Boston | Dallas | Denver | Chicago | Houston | Jacksonville | Los Angeles | Miami | New York City | Phoenix | Portland | Sacramento | Salt Lake City | San Diego | San Francisco | Seattle | Washington D.C.

Canada: Ottawa | Toronto | Vancouver

Mexico: Mexico City

Benefits, Perks, and Wellbeing at Scribd

Benefits/perks listed may vary depending on the nature of your employment with Scribd and the geographical location where you work.

• Healthcare Insurance Coverage (Medical/Dental/Vision): 100% paid for employees
• 12 weeks paid parental leave
• Short-term/long-term disability plans
• 401k/RSP matching
• Onboarding stipend for home office peripherals + accessories
• Learning & Development allowance
• Learning & Development programs
• Quarterly stipend for Wellness, WiFi, etc.
• Mental Health support & resources
• Free subscription to the Scribd Inc. suite of products
• 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 Employee Resource Groups and programs that foster an inclusive and diverse workplace.
• Access to AI Tools: We provide free access to best-in-class AI tools, empowering you to boost productivity, streamline workflows, and accelerate bold innovation.

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