Data Scientist - eBay Live

eBay•San Jose, CA

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 experience where sellers and creators stream in real time and buyers engage through chat, bidding, and instant purchases. It blends entertainment, community, and commerce into a dynamic, trust‑backed way to discover and shop. Join us to build the analytics and AI backbone that powers discovery, engagement, and quality moderation across Live. It’s a high‑visibility, priority growth initiative with significant runway - an opportunity to deliver measurable impact at marketplace scale. Opportunity As a Staff Data Scientist, you will be both a highly skilled analyst and a beginning team leader - owning end‑to‑end analytical projects, scoping ambiguous problems, selecting the right methods, ensuring quality, and presenting insights to drive action. You will also set standards for analytical best practices, review team outputs, and architect solutions (e.g., data pipelines, tracking schemas) in partnership with engineering to scale product‑impacting analytics for eBay Live. Specifically you will be part of the journey to build the Data Science engine that powers eBay Live hyper‑growth across Seller Product & Seller Success, Buyer Product, Categories/Markets expansion, Trust & Safety, Data Foundation, and Business Performance - delivering measurable outcomes through causal measurement, experimentation, marketplace and risk modeling, and production‑grade insights/dashboards.

Requirements

MS in a quantitative field (e.g., Statistics, Economics, CS) or equivalent experience
typically 7+ years in analytics/data science with multiple production‑quality projects or complex analytical programs.
Strong SQL/Python
experimentation design
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

Buyer Product Analytics: Own end‑to‑end analyses for live commerce features; define product metrics and tracking; lead experiments that inform roadmap and growth; review peers’ work to uphold rigor.
Seller Product & Seller Success: Design and govern growth experiments; analyze onboarding, listing quality, conversion, and retention; propose interventions and measurement plans; enforce review standards and reproducibility.
Categories & Markets Expansion: Model category–market dynamics; size opportunities; build selection/expansion frameworks; advise sequencing and launch decisions; architect lightweight data assets (e.g., derived tables) to unblock analyses.
Trust & Safety: Analyze marketplace risk signals; partner on guardrail design and monitoring; apply practical ML/AI; refine test designs and metrics to reduce false positives while protecting good actors.
Data Foundation & Instrumentation: Clarify metric/event taxonomies; ensure instrumentation quality; build curated datasets and dashboards; troubleshoot data issues; automate recurring tasks with a product‑first mindset.
Business Performance: Own KPIs within scope; synthesize daily/weekly/monthly/quarterly performance narratives; surface opportunities/risks; influence decisions with evidence‑based recommendations.
Lead analyses end‑to‑end, from scoping to presentation; select robust methods (advanced experimentation, econometrics, statistical modeling) for complex measurements.
Establish and enforce coding, analysis, and verification best practices; conduct code/analysis reviews; ensure stakeholder‑ready outputs.
Identify and drive data solution needs (new pipelines, tracking schemas, derived tables) with engineering; design for scale and maintainability.
Spot high‑impact opportunities for ML/AI and automation; prototype solutions and partner with specialists to productionize where appropriate.
Distill complex analyses into crisp narratives; present insights and recommendations to senior stakeholders (directors/VPs) to drive decisions.
Operate autonomously as the primary analytics partner for your area; prioritize high‑impact work; manage cross‑functional project delivery to outcomes.
Balance hands-on contributions with mentoring and guidance; set hiring/engagement standards; build a culture of continuous learning and positivity.