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San Francisco, California, United States
Onsite
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 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.
You've questioned an existing approach and built something better rather than copying what was done before.
You've owned outcomes end-to-end, not just tasks within a narrow scope.
You've stepped outside your job description to solve a problem because it needed to get done.
You use Whatnot as a buyer or seller and can speak to the product with real context.
You've worked on a marketplace, platform, or product where meaningfully different user groups required different product experiences or analytical approaches.
You've made consequential product recommendations in situations where the data was incomplete, ambiguous, or statistically challenging.
Compressed career growth. Short feedback loops, real ownership, and problems no one has solved before mean you learn faster. People regularly say one year here equals three elsewhere, and high performers take on more responsibility quickly.
Category-defining work. Live commerce at this scale has never been done in the West. Your work shapes how an entirely new industry develops.
Exceptional colleagues. We're deliberate about talent density. Your coworkers will challenge your thinking, raise your standards, and make you better.
Real ownership. You'll be expected to form opinions, influence decisions, and own outcomes - not just execute a queue of analytical requests.
Tangible seller impact. Sellers are the supply side of our marketplace and are building real businesses on Whatnot. Helping them onboard, operate more efficiently, and grow is fundamental to the company's growth.
Intellectually difficult problems. Seller data is often heterogeneous, sparse, and deeply tied to real-world business behavior. The interesting questions rarely reduce to a single metric or obvious experiment.
AI-forward tools and workflows. We invest in modern AI tools and expect them to be part of daily work. The important skill is increasingly not just writing code, but knowing what questions to ask, moving quickly, and recognizing when the output is, or isn't, trustworthy.
Rewarding collaboration. The work is challenging, but the people are great and wins get celebrated.
Generous Holiday and Time off Policy
Health Insurance options including Medical, Dental, Vision
Work From Home Support
Home office setup allowance
Monthly allowance for cell phone and internet
Care benefits
Monthly allowance for wellness
Annual allowance towards Childcare
Lifetime benefit for family planning, such as adoption or fertility expenses
Retirement; 401k offering for Traditional and Roth accounts in the US (employer match up to 4% of base salary) and Pension plans internationally
Monthly allowance to dogfood the app
All Whatnauts are expected to develop a deep understanding of our product. We're passionate about building the best user experience, and all employees are expected to use Whatnot as both a buyer and a seller as part of their job (our dogfooding budget makes this fun and easy!).
Parental Leave
16 weeks of paid parental leave + one month gradual return to work *company leave allowances run concurrently with country leave requirements which take precedence.
Please note: Whatnot will only contact you through official @whatnot.com email addresses. If you see an email impersonating a Whatnot recruiter, please disregard and report it as spam.
Whatnot is proud to be an Equal Opportunity Employer. We value diversity, and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, parental status, disability status, or any other status protected by local law. We believe that our work is better and our company culture is improved when we encourage, support, and respect the different skills and experiences represented within our workforce.
