Manager, Data Science

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 the team and the role

The Consumer-to-Consumer (C2C) team focuses on making selling and buying locally simpler, more relevant, and more trusted. The team works across search and discovery, local marketplace experiences, payments, and messaging to identify customer problems, shape product strategy, and improve marketplace outcomes.

As Manager, Data Science - Analytics 1 in the Indian Analytics Centre, you will be a strategic analytics partner for the C2C Local, M2M and adjacent product areas. You will operate in a highly dynamic environment, take broad or ambiguous business questions, structure them into answerable analytical problems, and convert complex findings into compelling narratives and clear actions. The role combines advanced hands-on analytics with strong influence over collaborators. It requires someone who is dedicated, proactive, outgoing, and comfortable crafting clarity when the path is not predefined.

What you will accomplish

- Own strategic and product analytics for C2C Local and related customer journeys, including search ranking, local discovery, distance accuracy, marketplace flows, payments, and messaging experiences.

- Translate broad leadership and product questions into structured analytical plans, clear hypotheses, measurable success criteria, and actionable recommendations.

- Use advanced SQL and deep analytical rigour to evaluate customer behaviour, product performance, feature adoption, funnels, cohorts, and marketplace outcomes across complex datasets.

- Build persuasive, insight-led narratives that connect data, customer context, product trade-offs, and business impact for both working teams and senior stakeholders.

- Partner with product, engineering, design, operations, and peer analytics teams to shape priorities, align stakeholders, and drive decisions in areas where ownership or direction may initially be unclear.