Data Scientist, Whatnot

• 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
• 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 recommendations
• Deep knowledge of experimentation, causal inference, and statistical methods, including approaches appropriate for smaller or noisier datasets such as Bayesian methods
• Strong statistical judgment: you understand what the data can support, what it can't, and when additional product or business judgment is required
• Advanced SQL skills and working proficiency in Python. You don't need to be a software engineer, but you should be comfortable using code to investigate data, perform statistical analysis, and evaluate or debug AI-assisted work
• Experience working closely with Product and Engineering teams and influencing decisions rather than simply delivering analysis
• The ability to disagree constructively: you can push back when the evidence warrants it, communicate uncertainty clearly, and maintain strong working relationships with cross-functional partners
• A track record of independently identifying opportunities and driving analytical work from problem framing through recommendation and impact
• Experience with e-commerce, merchant products, inventory systems, seller tools, or two-sided marketplaces is a plus, but not required

Desirable

• 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
• 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