Data Scientist, Seller Merchant

Whatnot is the largest live shopping platform in North America and Europe to buy, sell, and discover the things you love. Whether it's trading cards, fashion, electronics, or live plants, our sellers are building real businesses across hundreds of categories. We're building live commerce at a scale that's never been done in the West, and there's no playbook to copy. The people here are shaping how an entirely new industry develops.

As a remote co-located team, we're inspired by our values and anchored in hubs across the US, UK, Ireland, Poland, Germany, and Australia. We move fast, stay close to our users, and focus on the work that drives the most impact.

We're one of the fastest growing marketplaces and were recently named the #1 Best Startup Employer in America by Forbes. Check out the latest Whatnot updates on our news and engineering blogs and join us as we enable anyone to turn their passion into a business and bring people together through commerce.

The Seller Merchant team helps sellers bring inventory onto Whatnot, organize it effectively, and make it easier to sell. That includes products, listings, variants, catalog, taxonomy, inventory creation, seller workflows, and integrations such as Shopify.

Our sellers range from someone going live for the first time on their phone to sophisticated merchants managing large inventories across multiple systems. They also operate across very different categories, countries, and selling formats. The right product experience for one seller may be completely wrong for another.

As a Data Scientist on Seller Merchant, you'll use data to help us understand those differences and build better products for sellers. Your work will ultimately help sellers become more productive, grow their businesses, and bring more high-quality supply to the marketplace.

You'll:

• Identify and prioritize the highest-impact opportunities to improve seller success. Use product analysis, opportunity sizing, and strategic thinking to understand where changes to seller workflows can have the largest impact on activation, inventory creation, selling efficiency, and GMV.

• Design and evaluate experiments across the seller experience. Partner with Product, Engineering, and Design to turn ambiguous questions into testable hypotheses, define success metrics and guardrails, design rigorous experiments, and translate results into product decisions.

• Develop the analytical frameworks that shape product strategy. Go beyond answering incoming questions by proactively identifying opportunities, defining new ways to measure seller success, and helping the team decide what to build next.

• Understand how different sellers need different products. Analyze differences across seller size, maturity, category, country, inventory type, and selling format, and help determine when a single experience works versus when we should build for distinct seller segments.

• Help us make good decisions even when the data isn't perfect. Supply-side problems often involve smaller samples, heterogeneous populations, or decisions where data provides important evidence without giving a black-and-white answer. You'll apply analytics, causal inference, and other statistical approaches where appropriate, while partnering closely with product leaders to combine evidence with sound judgment.

You'll be deeply embedded with the product team, but you'll also have meaningful independence to develop your own hypotheses, challenge existing thinking, and use data to influence where the team invests.

We offer flexibility to work from home or from one of our global office hubs, and we value in-person time for planning, problem-solving, and connection. Team members in this role must live within commuting distance of our New York, San Francisco hub.

People who do well at Whatnot tend to be comfortable figuring things out as they go, biased toward action, and genuinely curious about what they're building. They care more about outcomes than credit and stay close to the product and the people using it.

As our next Data Scientist, you should have 5+ years of experience in product data science, analytics, or a closely related field, plus:

• A Bachelor's degree in Statistics, Economics, Computer Science, Mathematics, a related quantitative field, or equivalent relevant technical training.

• Strong product and business judgment, with experience translating ambiguous product questions into hypotheses, analyses, experiments, and…