Data Scientist

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.

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.

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.

You will focus on one of the following pillars, while coaching and raising the bar for analysis quality:

• 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.

• Technical leadership and depth:
  Lead analyses end‑to‑end, from scoping 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 best practices; conduct code/analysis reviews; ensure stakeholder‑ready outputs.
• Architecting solutions:
  Identify and drive data solution needs (new pipelines, tracking schemas, derived tables) with engineering; design for scale and maintainability.
• Applied AI/ML integration:
  Spot high‑impact opportunities for ML/AI and…