Data Scientist developing machine learning and AI solutions at STATSports. Turning GPS and IMU sensor data into actionable insights for athlete performance and decision - making.
About the role
- Lead Data Scientist at Scribd focused on measurable outcomes and AI for recommendation engines. Collaborating cross-functionally to define metrics and drive business results.
Responsibilities
- 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.
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.
Benefits
- 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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