Data Scientist 1 , Knowledge Management

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 Team and Role

We’re building the future of eCommerce product discovery, and we need a data-driven, AI-curious problem solver to join our team.

This is an exciting, hands-on role at the intersection of data analytics, AI/ML experimentation, and prompt engineering—ideal for an early-career professional who loves writing SQL queries, building Python scripts, and experimenting with LLMs to solve real-world problems. You’ll be embedded in the Product Knowledge org, helping us clean, structure, and optimize taxonomy, ontology, and catalog data for next-gen search and recommendation engines.

If you are eager to learn, thrive in fast-moving environments, and want to work directly with AI/ML, Product, and Engineering teams, this is the perfect place to accelerate your career.

What You’ll Do

- Analyze eCommerce Product Data: Run SQL queries and Python scripts to analyze taxonomy, ontology, and catalog data, identifying gaps and opportunities for search improvement.

- Experiment with LLMs: Assist in building and testing prompt engineering workflows to automate data enrichment, product classification, and taxonomy validation.

- Support Model Evaluation: Help test and evaluate the performance of product knowledge models, semantic search solutions, and embeddings against defined metrics.

- Collaborate Cross-Functionally: Work closely with data scientists, product managers, and engineers to execute data strategies and refine AI-based classification models.

- Build Quick Prototypes: Move fast to test data hypotheses, run experiments, and help validate new structured data ideas for better product discovery.