Data Scientist (Analytics) - L24

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.

We are seeking a Data Scientist (Analytics) to join the eBay Live Buyer Experience team, supporting eBay’s real-time livestream shopping experience across categories such as Collectibles, Luxury, Sneakers, and Apparel.

As eBay Live continues to scale, this role will help shape the buyer experience through product analytics and experimentation. You will partner closely with Product, Engineering, Design, and business teams to understand buyer behavior, evaluate new experiences, and identify opportunities to improve discovery, engagement, conversion, and retention.

This is a hands-on individual contributor role for someone who enjoys working with large behavioral datasets, designing rigorous experiments, and translating analysis into actionable product recommendations.

Key Responsibilities

• Own product analytics for key areas of the eBay Live buyer journey, including discovery, viewing, engagement, bidding, purchasing, and retention.

• Partner with Product and Engineering teams to define success metrics, KPIs, and guardrails for new features and experiences.

• Design, analyze, and interpret A/B tests and controlled experiments, ensuring statistical rigor and clear decision criteria.

• Apply advanced measurement approaches, including causal inference and quasi-experimental methods, when traditional A/B testing is not feasible.