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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 understanding—shaping how millions engage with our global content library.
Job Responsibility:
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
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
Requirements:
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
What we offer:
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