Senior Manager eBay Live Data Science

eBay•San Jose, CA

2d•$170,400 - $227,500•Remote

About The Position

At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts. Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet. Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all. About eBay Live eBay Live is eBay’s interactive live shopping platform where sellers and creators broadcast in real time and buyers participate through chat, bidding, and instant purchases. It combines entertainment, community, and commerce into an engaging, trust‑based approach to discover and shop. Join us to develop the analytics and AI foundation that drives discovery, engagement, and quality moderation throughout Live. It’s a highly visible, priority growth project with substantial potential—a chance to create measurable impact at marketplace scale. Opportunity In your role as a Staff Data Scientist, you will combine advanced analysis skills with initial leadership responsibilities. You will manage complete analytical projects, prioritize unclear problems, choose suitable methods, maintain quality, and share insights that drive decisions. Additionally, you will define protocols for analysis, evaluate outputs from the team, and develop solutions (including data pipelines and tracking schemas) alongside engineering to expand product-impact analytics for eBay Live. Specifically, you will contribute to developing the Data Science engine that drives eBay Live’s rapid growth across Seller Product & Seller Success, Buyer Product, Categories/Markets expansion, Trust & Safety, Data Foundation, and Business Performance—providing measurable results through causal measurement, experimentation, marketplace and risk modeling, and production‑quality insights/dashboards.

Requirements

Technical leadership and depth: Lead analyses end‑to‑end, from prioritization to presentation; select robust methods (advanced experimentation, econometrics, statistical modeling) for complex measurements.
Review standards and quality bar: Establish and enforce coding, analysis, and verification guidelines; conduct code/analysis reviews; ensure recipient‑ready outputs.
Architecting solutions: Identify and drive data solution needs (new pipelines, tracking schemas, derived tables) with engineering; build for scale and maintainability.
Applied AI/ML integration: Spot high‑impact opportunities for ML/AI and automation; prototype solutions and partner with specialists to productionize where appropriate.
Influential communication: Distill complex analyses into crisp narratives; present insights and recommendations to senior collaborators (directors/VPs) to drive decisions.
Ownership of domain and collaborator management: Operate autonomously as the primary analytics partner for your area; prioritize high‑impact work; manage cross‑functional project delivery to outcomes.
Player‑coach leadership: Balance hands‑on contributions with mentoring and guidance; set hiring/engagement standards; build a culture of continuous learning and positivity.
MS in a quantitative field (e.g., Statistics, Economics, CS) or equivalent experience.
Typically, 7+ years in analytics or data science with multiple production-quality projects or complex analytical programs.
Strong SQL/Python
Experimentation build
Statistical/econometric modeling
Dashboarding
Familiarity with marketplace dynamics and product analytics.
Executive‑ready storytelling that translates analysis into actionable recommendations and influences product decisions.

Responsibilities

Own end-to-end analyses for live commerce features.
Define product metrics and tracking.
Lead experiments that inform the roadmap and growth.
Review peers’ work to uphold rigor.
Build and govern growth experiments; analyze onboarding, listing quality, conversion, and retention; propose interventions and measurement plans; enforce review standards and reproducibility.
Model category–market dynamics and size opportunities.
Build selection and expansion frameworks.
Advise sequencing and launch decisions.
Architect lightweight data assets, such as derived tables, to unblock analyses.
Analyze marketplace risk signals and collaborate on guardrail building and monitoring.
Apply practical ML/AI techniques.
Refine test builds and metrics to reduce false positives while protecting good actors.
Clarify metric/event taxonomies; ensure instrumentation quality; build curated datasets and dashboards; troubleshoot data issues; automate recurring tasks with a product‑first approach.
Own important metrics within scope; synthesize daily/weekly/monthly/quarterly performance narratives; surface opportunities/risks; influence decisions with evidence‑based recommendations.