Junior Data Scientist

Junior Data Scientist at Whatnot — Research Report

Introduction

The Junior Data Scientist role at Whatnot offers a high-impact entry point into live commerce analytics, where your models directly influence buyer behavior and seller success. With a Remote (New York, NY) setup and no specified deadline, this opportunity is ideal for rising juniors or seniors eager to build scalable AI solutions in a fast-growing tech environment. Securing a Whatnot internship here can accelerate your career by providing exposure to real-world product problems and a potential path to full-time conversion.

Overview of Whatnot

Whatnot is the leading live-commerce platform in the United States, enabling sellers to broadcast real-time video auctions for collectibles, fashion, and entertainment. The company dominates its niche by combining the excitement of live TV with the convenience of mobile shopping, creating a unique marketplace for high-demand items like Pokémon cards and vintage sneakers. Competitors include traditional auction sites and emerging live-streaming apps, but Whatnot’s focus on community and authenticity gives it a distinct edge.

The platform has achieved massive market presence, processing billions in gross merchandise value (GMV) and expanding globally into Canada, the UK, and Australia. Whatnot’s culture is described as energetic, data-driven, and collaborative, with a reputation for empowering employees to take ownership of projects. People want to work here because of the company’s rapid growth, innovative product, and the chance to solve complex challenges in a sector that is reshaping how consumers buy and sell.

Junior Data Scientist Role

Role Overview

As a Junior Data Scientist, you will design and deploy data-driven solutions that address real-world challenges in live commerce, such as optimizing auction algorithms or predicting seller demand. Your work directly impacts product strategy by translating raw data into actionable insights for cross-functional teams like engineering and product management. This role is critical for building scalable analytics pipelines and developing machine learning models that enhance the user experience.

Detailed Responsibilities

• Build and maintain scalable analytics pipelines to process large datasets from live streams and transactions.
• Develop machine learning models for tasks like recommendation systems, fraud detection, and demand forecasting.
• Design and analyze A/B tests to evaluate new product features and measure their impact on key metrics.
• Translate complex data findings into clear, actionable recommendations for non-technical stakeholders.
• Collaborate with data engineers to ensure data quality and efficient pipeline performance.

Day-to-Day Workflow

Your day typically starts with reviewing data dashboards to monitor key performance indicators like GMV and user engagement. You will spend significant time writing SQL queries to extract data from warehouses like Snowflake or BigQuery, followed by Python scripting for analysis and modeling. Mid-day often involves cross-functional meetings with product managers to align on project goals, while the afternoon focuses on prototyping new models or refining existing ones. The workflow is iterative, with frequent feedback loops to ensure your solutions meet business needs.

Tools and Technologies

Whatnot’s tech stack includes modern data warehouses such as Snowflake, BigQuery, and Redshift, paired with tools like Spark and DBT for data transformation. You will use Python (preferred) or R for analysis, modeling, and experimentation, along with libraries like Pandas, NumPy, and Scikit-learn. For visualization, the company relies on BI tools like Tableau or Power BI to build interactive dashboards. Cloud platforms such as AWS or GCP are also integral for deploying models at scale.

Skills and Requirements

Technical Skills

Candidates must have advanced SQL skills and proficiency in Python for data analysis and modeling. A strong foundation in statistics, machine learning, and A/B testing is essential, along with familiarity with data visualization tools. Experience with cloud platforms and software development practices like Git is a plus, as is the ability to handle large datasets with attention to detail.

Soft Skills

Whatnot prioritizes curiosity and initiative over perfection, seeking candidates who can communicate complex ideas clearly to diverse stakeholders. Strong problem-solving and critical thinking abilities are crucial, as is the capacity to work effectively in cross-functional teams. The role demands adaptability to new tools and a collaborative mindset to drive product strategy with data.

Experience Expectations

Whatnot targets rising juniors or seniors with 0–2 years of experience, including internships or academic projects. A bachelor’s degree in Computer Science, Economics, Statistics, or a related quantitative field is required, but no graduate degree is needed. A strong portfolio showcasing data projects, a solid GPA, and demonstrated initiative in solving real-world problems will significantly boost your chances.

Salary and Benefits

For a Junior Data Scientist in New York, NY, the estimated pay range is $65,000–$85,000 annually for full-time roles, with internships offering around $24.73 per hour based on market data. Whatnot provides comprehensive perks including 401k matching, health/dental/vision insurance, flexible unlimited PTO, and paid holidays. Additional benefits include a home office stipend, health and wellness programs, and family/dependent insurance. Full-time conversion opportunities are common for high-performing interns, offering a clear path to long-term career growth.

Whatnot Hiring Process

Step-by-Step Hiring Stages

1. Application: Submit your resume, cover letter, and portfolio via the company career site.
2. Screening: ATS filters for keywords like SQL, Python, and machine learning; portfolio review by recruiters.
3. Assignment: Complete a data analysis or modeling task relevant to live commerce challenges.
4. Interviews: Participate in 2–3 rounds including technical assessments, behavioral interviews, and a case study.
5. Offer: Receive a formal offer with details on compensation, benefits, and start date.

Application Timeline

Apply early, as deadlines are not specified but roles fill quickly. The typical process duration is 3–5 weeks from application to offer, with screening taking 1–2 weeks and interviews spanning 2–3 weeks. Timing your application during peak hiring seasons (spring or fall) may improve visibility.

Screening Methods

Whatnot uses an ATS to scan for keywords like SQL, Python, A/B testing, and machine learning. Your portfolio must demonstrate practical experience with data projects, and a strong GPA can help pass initial filters. Recruiters also look for evidence of cross-functional collaboration and the ability to translate data into business impact.

Interview Preparation

Example Interview Questions

• How would you design an A/B test to evaluate a new feature that changes the auction bidding process?
• Describe a machine learning model you built to solve a real-world problem and its impact.
• How do you handle missing data in a large dataset when building a predictive model?
• Explain how you would communicate complex data insights to a non-technical product manager.

How to Answer

Use the STAR method (Situation, Task, Action, Result) to structure your responses, focusing on concrete examples and measurable outcomes. For technical questions, walk through your thought process step-by-step, explaining your choice of tools and methods. Emphasize how your work drove business value, such as increasing GMV or improving user engagement.

What Recruiters Evaluate

Recruiters assess your technical proficiency in SQL and Python, your ability to design and analyze experiments, and your communication skills. They also evaluate your curiosity, initiative, and capacity to collaborate across teams. Key criteria include problem-solving agility, attention to detail, and the ability to translate data into actionable insights.

How to Get Selected

Practical Tips

• Tailor your resume to highlight SQL, Python, and machine learning projects with clear business impact.
• Build a portfolio showcasing live commerce-related data projects, such as auction optimization or demand forecasting.
• Network with Whatnot employees on LinkedIn to gain insider insights and potentially secure a referral.
• Practice A/B test design and causal inference techniques, as these are critical for the role.

Common Mistakes to Avoid

• Submitting a generic resume without keywords like SQL, Python, or machine learning.
• Failing to demonstrate real-world problem-solving experience in your portfolio.
• Overlooking the importance of communication skills in behavioral interviews.
• Applying late when roles are already filled due to the lack of a specified deadline.

How to Stand Out

Stand out by creating a tailored portfolio that addresses live commerce challenges, such as optimizing seller auctions or predicting buyer trends. Reach out to Whatnot recruiters or employees for networking opportunities, and submit a customized application that aligns your skills with the company’s mission. Highlight any experience with A/B testing or causal inference, as these are highly valued.

Final Thoughts

The Junior Data Scientist role at Whatnot is a gateway to shaping the future of live commerce with data-driven innovation. By mastering the technical skills, showcasing real-world impact, and embracing Whatnot’s culture of curiosity, you can secure a spot in this dynamic team. Take action now: refine your portfolio, network strategically, and apply early to maximize your chances of getting hired.

Frequently Asked Questions

Q: What is the salary for Junior Data Scientist at Whatnot?
A: The estimated annual salary is $65,000–$85,000 for full-time roles, with internships offering around $24.73 per hour in New York, NY.

Q: How competitive is it to get hired at Whatnot?
A: It is competitive due to the company’s rapid growth and high demand for data talent, but targeting rising juniors/seniors with strong portfolios improves your odds.

Q: What skills are most important for this role?
A: Advanced SQL, Python proficiency, machine learning basics, A/B testing, and the ability to communicate data insights clearly are critical.