Whatnot

We’re innovating in the fast-paced world of live auctions in categories including sports, fashion, video games, and streetwear.. This can include: chargeback prevention, refund abuse detection and reduction, and measurement to capture the tradeoffs in reducing fraudulent and unsafe behavior on the platform. Analyze the effectiveness of existing methods and partner with Machine Learning and Trust & Safety teams to develop better anti-fraud practices.. 3+ years of experience in Data Analytics & Science supporting anti-fraud, risk, trust & safety, or integrity problems.. Comfortability with data warehouses and big data technologies such as Redshift, Snowflake, Big Query, Presto/Trino, Athena, Spark, DBT

data scientist

Whatnot Whatnot is a livestream shopping platform and marketplace. We’re building the future of ecommerce, bringing together community, shopping and entertainment. We are committed to our values, and as a remote-first team, we operate out of hubs within the US, Canada, UK, Ireland, and Germany today.
We’re innovating in the fast-paced world of live auctions in categories including sports, fashion, video games, and streetwear. The platform couples rigorous seller vetting with a focus on community to create a welcoming space for buyers and sellers to share their passions with others.
And, we’re growing. Whatnot has been the fastest growing marketplace in the US over the past two years and we’re hiring forward-thinking problem solvers across all functional areas.
Role In order to continue this growth, it’s important that Whatnot remains a safe and trusted space to interact and transact. We’re looking for a Data Scientist with expertise in fraud and risk to detect and prevent these threats to our community. You will:
Create rules and systems to enhance the full lifecycle of risk and fraud measurement. This can include: chargeback prevention, refund abuse detection and reduction, and measurement to capture the tradeoffs in reducing fraudulent and unsafe behavior on the platform
Define and own the experimentation playbook for Fraud at Whatnot.
Partner closely across the business to find improvements/opportunities and influence decisions using data science methodologies and tools.
Build actionable KPIs, create production-quality dashboards and notebooks to convey insights.
Define and advance best practices to enable system-level solutions to reduce fraud and payment risk.
Analyze the effectiveness of existing methods and partner with Machine Learning and Trust & Safety teams to develop better anti-fraud practices.
Inform the engineering, operations, and machine learning roadmaps through analysis of marketplace, user behavior, and product trends.
About You We are looking for intellectually curious, highly motivated individuals to be foundational members of our Data team! You will partner with our Engineering, Product, and Operations teams to systematically detect, measure, and action against fraud and other threats to Whatnot’s platform.
You should have strong critical thinking and analytical skills, excellent communication abilities, and a knack for working across teams in a fast-paced environment. The ideal candidate will be adept in navigating the data stack and able to support initiatives in all facets from analytics/data engineering and product analytics to machine learning, ideally with previous experience within fraud-adjacent domains.
In addition to 5+ years of experience in the Data field, you should have:
3+ years of experience in Data Analytics & Science supporting anti-fraud, risk, trust & safety, or integrity problems.
Excellent verbal communications, including the ability to clearly and concisely articulate complex concepts to both technical and non-technical collaborators
Bachelor’s degree in Computer Science, Economics, Statistics, or a related field, or equivalent work experience.
Industry experience with proven ability to apply scientific methods to solve real-world problems on large scale data
Ability to lead initiatives across multiple product areas and communicate findings with leadership and product teams
Comfortability with data warehouses and big data technologies such as Redshift, Snowflake, Big Query, Presto/Trino, Athena, Spark, DBT
Expert in using SQL for data analysis, reporting, and dashboarding
Experience with a scripting language such as Python or R
Aptitude and experience in applied statistical modeling and machine learning techniques
Firm grasp of visualization tools, interactive and self-serving, such as business intelligence and notebooks
Compensation For US-based applicants:$180,000 - $230,000/year + benefits + stock options
The salary range may be inclusive of several levels that would be applicable to the position. Final salary will be based on a number of factors including, level, relevant prior experience, skills and expertise. This range is only inclusive of base salary, not benefits (more details below) or equity in the form of stock options.
Benefits Flexible Time off Policy and Company-wide Holidays (including a spring and winter break)
Health Insurance options including Medical, Dental, Vision
Work From Home Support
$1,000 home office setup allowance
$150 monthly allowance for cell phone and internet
Care benefits
$450 monthly allowance on food
$500 monthly allowance for wellness
$5,000 annual allowance towards Childcare
$20,000 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
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.
EOE 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.

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