What is a Data Scientist at Whatnot?

At Whatnot, the Data Scientist role is far more than just querying databases or building isolated models. You are entering a fast-paced, high-growth environment where data is the compass for strategic decision-making. Whatnot sits at the unique intersection of community, live entertainment, and e-commerce. This creates a complex, noisy, and fascinating dataset that requires you to disentangle user behavior from marketplace dynamics.

In this position, you act as a strategic partner to Product, Engineering, and Operations. Whether you are working on Product Analytics (optimizing the live auction experience), Marketplace Integrity (detecting fraud and ensuring trust), or Category Expansion (launching new markets like Germany or new categories like sneakers), your core mission is to translate ambiguous business questions into actionable insights. You will define the KPIs that matter, design the experiments that guide feature rollouts, and build the tools that empower the rest of the company to move fast.

The team values individuals who can balance analytical rigor with speed. You are expected to be a "full-stack" data scientist—capable of handling data engineering tasks to clean your own pipelines, performing deep statistical analysis, and then presenting your findings to leadership with clarity and business context.

Getting Ready for Your Interviews

Preparation for Whatnot is about demonstrating that you can execute quickly without sacrificing accuracy. The interviewers are looking for practitioners who have strong technical foundations but are ultimately driven by business impact.

Technical Fluency – You must be extremely comfortable with SQL and Python (or R). Whatnot moves fast; interviewers expect you to write clean, efficient query code in real-time. You should be able to manipulate data, perform complex joins, and clean datasets without constantly needing to look up syntax.

Product Sense & Experimentation – A significant portion of the interview process focuses on your ability to apply data to product problems. You will be evaluated on how you design A/B tests, how you select metrics (guardrail vs. success metrics), and how you investigate root causes when metrics shift unexpectedly.

Communication & Influence – You will often work with non-technical stakeholders. Interviewers assess your ability to explain complex statistical concepts simply and your ability to "influence without authority." You need to show that you can advocate for data-driven decisions even when the path forward isn't obvious.

Culture Fit (Low Ego, High Impact) – Whatnot prides itself on a culture of "low ego." You should demonstrate a growth mindset, a willingness to get your hands dirty (e.g., "dogfooding" the app), and a focus on shipping value rather than perfecting academic models that never see production.

Interview Process Overview

The interview process at Whatnot is known for being streamlined and relatively fast-paced, often concluding within 2 to 4 weeks, though this can vary by team availability. The process is designed to test your practical skills in conditions that mimic the actual job. It generally begins with a Recruiter Screen to align on timelines and interests, followed by a Hiring Manager Screen which often includes a "mini-case" study to test your product intuition early on.

If you pass the initial screens, you will move to a Technical Screen. This is almost exclusively focused on SQL and Python coding. Candidates have reported that this round can be time-pressured, often requiring you to solve multiple problems in a short window (e.g., 30–45 minutes). Speed and syntax mastery are critical here.

The final stage is a Virtual Onsite loop, usually consisting of 3–4 separate rounds. These rounds are split between deep-dive technical skills (Data Transformation, Experiment Design) and behavioral/collaborative assessments. You should expect at least one dedicated "Case Study" round where you will tackle an open-ended business problem. The process is rigorous but professional, with recruiters typically providing prompt feedback.

This timeline illustrates the typical funnel. Note the heavy emphasis on the "Virtual Onsite," which is a marathon of back-to-back sessions. You should plan your energy levels accordingly, as you will be switching contexts between coding, statistical theory, and high-level business strategy throughout the day.

Deep Dive into Evaluation Areas

To succeed, you need to master specific evaluation pillars. Based on candidate data and job requirements, the following areas are the most critical for the Data Scientist role.

SQL and Data Manipulation

This is the most fundamental filter in the Whatnot process. You will not pass if your SQL is rusty. Interviewers look for the ability to write complex queries from scratch. Be ready to go over:

• Complex Joins & Aggregations – Self-joins, cross-joins, and handling one-to-many relationships in a marketplace context (buyers vs. sellers).
• Window Functions – Ranking, moving averages, and cumulative sums (RANK, LEAD, LAG).
• Data Cleaning – Handling NULLs, casting types, and dealing with messy, real-world log data.
• Optimization – Writing queries that are not just correct, but efficient.

Example scenarios:

• "Write a query to calculate the retention rate of sellers who started in January vs. February."
• "Identify the top 3 buyers by spend in each category for the last month."

Product Analytics & Experimentation

This area tests your ability to drive product strategy through data. You need to show you understand the "why" behind the data. Be ready to go over:

• Metric Definition – defining North Star metrics, counter-metrics, and guardrail metrics for new features.
• A/B Testing – Sample size calculation, randomization units, duration, and analyzing results (statistical significance vs. practical significance).
• Root Cause Analysis – Systematically debugging why a key metric (like Daily Active Users) suddenly dropped.

Example scenarios:

• "We want to launch a new 'Buy It Now' feature in auctions. How would you design the experiment?"
• "Average watch time on streams has dropped by 10%. How do you investigate?"

Business Case Studies

These interviews are conversational and open-ended. They test your business acumen and structured thinking. Be ready to go over:

• Marketplace Dynamics – Understanding supply vs. demand constraints, liquidity, and network effects.
• Strategic Trade-offs – Making decisions with incomplete data (e.g., "Should we prioritize growth or profitability in this new region?").
• Forecasting – Basic approaches to forecasting revenue or headcount planning (especially for International/Strategy roles).

Example scenarios:

• "How would you measure the success of our expansion into the German market?"
• "A specific category is seeing high fraud rates. What data would you look at to detect it without hurting good users?"

As shown in the word cloud, SQL, Experimentation, and Metrics are the dominant themes. While "Python" and "Modeling" appear, they are often secondary to the core ability to access data (SQL) and apply it to business logic (Case Study). Prioritize your prep accordingly.

Key Responsibilities

As a Data Scientist at Whatnot, your day-to-day work is highly cross-functional. You are rarely working in a silo. You will be embedded within a specific vertical—such as Product, International, or Marketplace Integrity—where you act as the data owner for that domain.

Your primary responsibility is to generate insights that shape product direction. This involves analyzing user behavior to identify friction points in the live shopping experience or opportunities for growth. You will translate these findings into structured recommendations for Product Managers and Engineering leads.

You will also drive experimentation and measurement. You are responsible for the end-to-end lifecycle of A/B tests: designing the test plan, calculating power, monitoring the rollout, and presenting the final "ship/no-ship" recommendation.

Beyond analysis, you will build data products and tools. This means creating automated dashboards (often in tools like Looker) that allow stakeholders to self-serve data. You will also maintain the data pipelines (using tools like dbt) to ensure the metrics you report on are reliable and up-to-date.

Role Requirements & Qualifications

Candidates are assessed against a specific profile that blends technical expertise with marketplace experience.

Must-Have Skills:

• Advanced SQL: Proficiency with modern data warehouses (Snowflake, BigQuery, Redshift). You must be able to write production-level SQL.
• Statistical Analysis: Strong grasp of A/B testing, causal inference, and hypothesis testing.
• Product Experience: 3+ years (typically) in a product-focused analytics role, ideally within a B2C or marketplace company.
• Scripting: Proficiency in Python or R for data manipulation (pandas, numpy) and visualization.

Nice-to-Have Skills:

• Marketplace Background: Experience specifically with two-sided marketplaces (supply/demand dynamics), e-commerce, or social media platforms.
• BI Tooling: Deep expertise in Looker (LookML) or Tableau.
• Data Engineering: Familiarity with dbt, Airflow, or Spark is a strong plus, as you will often own your own data pipelines.

Common Interview Questions

These questions are compiled from recent candidate experiences and reflect the actual rigor of the Whatnot interview process. Do not memorize answers; instead, use these to practice your structure and problem-solving approach.

Technical & SQL

• "Given a table of user logins and a table of purchases, write a query to find the users who made a purchase on their very first day of logging in."
• "Calculate the rolling 7-day active user count for each day in the past year."
• "How would you identify duplicate accounts that are being used for market manipulation?"
• "Write a query to find the top 10% of sellers by revenue for each month."

Product Sense & Metrics

• "We are noticing that users are watching streams but not bidding. What metrics would you look at to understand why?"
• "How would you measure the health of the 'Trading Card' category specifically?"
• "If we increase the shipping subsidy for buyers, how do we measure if it was profitable?"
• "Define a 'good' seller on Whatnot. How would you quantify that?"

Behavioral & Leadership

• "Tell me about a time you disagreed with a Product Manager about an experiment result. How did you resolve it?"
• "Describe a time you had to deliver bad news based on data. How did you communicate it?"
• "How do you prioritize your work when you have requests from Sales, Engineering, and Product all at once?"

These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Frequently Asked Questions

Q: How difficult is the SQL assessment? The SQL assessment is considered medium-to-hard, largely due to time constraints. You may have 30 minutes to solve 3–4 progressive problems. Candidates often fail not because they don't know the answer, but because they aren't fast enough. Practice writing queries against a timer.

Q: Does Whatnot offer remote work? Yes, Whatnot is a remote-first company with hubs in major cities (SF, NY, LA, etc.). Many roles are fully remote, though some specific positions may require you to be within commuting distance of a hub for occasional in-person planning.

Q: What is the biggest differentiator for successful candidates? Successful candidates show "Business Judgment." It is not enough to just pull the data; you must explain what it means for the business. Candidates who can link a statistical finding to a revenue or retention outcome tend to receive offers.

Q: How long does the process take? The team moves extremely fast. It is not uncommon to go from application to offer in 2–3 weeks. However, be prepared for a high volume of interviews packed into that short timeframe.

Other General Tips

Speed is a Feature: In the technical rounds, do not spend 5 minutes thinking silently. Start writing or talking immediately. The time limits are tight (sometimes 30 minutes for 4 questions), and getting cut off early is a common reason for rejection.

Clarify the "Case": In the case study rounds, never jump straight to a solution. Always ask clarifying questions first: "Are we focused on mobile or web?" "Is this for the US market or global?" "Is the goal revenue or engagement?" This "scoping" phase is graded.

Know the Product: Download the app and watch a live auction before your interview. Understanding the mechanics of "bidding," "giveaways," and "shipping" is crucial. You will likely be asked how you would improve a specific feature you encountered.

Summary & Next Steps

The Data Scientist role at Whatnot is a high-impact position that sits at the heart of a rapidly scaling marketplace. You will be challenged to solve complex problems regarding user behavior, auction dynamics, and international growth. The company values speed, clarity, and the ability to turn raw data into business strategy.

To succeed, focus your preparation on advanced SQL speed, A/B testing design, and marketplace metrics. Practice verbalizing your thought process for open-ended case studies, ensuring you always tie your analysis back to business value. This is a role for builders and problem solvers who want to see their insights implemented immediately.

The compensation for this role is competitive, reflecting the high bar for talent. The range provided typically includes base salary, but keep in mind that total compensation at Whatnot often includes significant equity components, which are a major part of the value proposition for joining a high-growth startup.

Approach the process with confidence. The interviewers are looking for reasons to hire you, not trick you. Demonstrate your passion for the product, your technical rigor, and your ability to move fast, and you will be well-positioned to land the offer.