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 Senior Data Scientist, you will independently operate as a trusted Analytics Thought Partner for a specific eBay Live pillar - leading moderate‑to‑complex analyses end‑to‑end, actively engaging stakeholders, resolving ambiguous problems, and prioritizing the most impactful work. Specifically you will be part of the journey to build the Data Science engine powering eBay Live hyper‑growth across pillars such as Seller Product and Success, Buyer Product, Categories/Markets expansion, Trust & Safety, Data Foundation, and Business Performance; and to deliver measurable impact in product strategy, growth, and marketplace efficiency through causal measurement, experimentation, marketplace and risk modeling, and production‑grade insights/dashboards.

Requirements

Advanced analytics and causal inference (uplift, diff‑in‑diff, CUPED), robust A/B test design, and strong judgment in method selection for ambiguous problems.
Technical toolkit: SQL/Python, dashboarding (e.g., Tableau/Looker), familiarity with applied ML/AI, and the ability to optimize queries and contribute lightweight data engineering when necessary.
Quality and rigor: Proactive assumption checks, validation, and troubleshooting; ability to scale insights via self‑serve dashboards and reporting that stakeholders actually use.
Communication and storytelling: Clear narratives that explain the “so what”; adjust depth for technical vs. non‑technical audiences; confident presentation and on‑the-fly Q&A in cross‑functional forums.
Stakeholder engagement: Proactive partner management; serve as the primary analytics contact for your area; propose data‑driven opportunities beyond explicit requests; consult managers for guidance in high‑stakes discussions as needed.
Emerging leadership: Lead analyses coordinating with others; mentor junior analysts on technical skills and domain; demonstrate ownership and reliability in delivery and outcomes.
BS/MS in a quantitative field (e.g., Statistics, Economics, CS) or equivalent experience; 4-7 years in analytics/data science with demonstrated end‑to‑end delivery on moderate‑to‑complex projects.
Strong SQL/Python; experimentation design; statistical modeling; dashboarding; familiarity with marketplace dynamics and product analytics.
Executive‑ready communication with the ability to distill complex analysis to actionable recommendations and influence product decisions.

Responsibilities

Lead end‑to‑end analyses for live commerce features
Define product metrics
Build dashboards
Run experiments that inform the roadmap and growth
Design growth experiments
Analyze onboarding, listing quality, conversion, and retention
Propose interventions
Track impact with rigorous measurement and reporting
Model category–market dynamics
Size opportunities
Build selection/expansion frameworks
Recommend sequencing and launch decisions
Contribute lightweight data engineering (e.g., derived tables) to unblock analyses
Analyze marketplace risk signals
Partner on guardrail design and monitoring
Apply practical ML/AI
Refine test designs and metrics to reduce false positives while preserving good‑actor experience
Clarify metric taxonomies
Ensure instrumentation quality
Build curated datasets and dashboards
Troubleshoot data issues
Automate recurring tasks with a product‑first mindset
Own KPIs within scope
Synthesize daily/weekly/monthly/quarterly performance narratives
Surface opportunities/risks
Influence decisions with evidence‑based recommendations and audience‑appropriate storytelling