Whatnot is the largest livestream shopping platform in North America and Europe to buy, sell, and discover the things you love. We’re building the future of ecommerce, bringing together community, shopping and entertainment. We are committed to our values, and as a remote co-located team, we operate out of hubs within the US, UK, Ireland, Poland, and Germany today.

We’re innovating in the fast-paced world of live auctions from fashion, beauty, electronics to collectibles like trading cards, comic books, and even live plants. Whatnot has something for everyone.

And, we’re growing. Whatnot has been one of the fastest growing marketplaces and we’re hiring forward-thinking problem solvers across all functional areas.

In Short

Define and own the KPIs that measure the Fraud, Risk, and Trust & Safety domains
Partner closely across the business to find improvements/opportunities and influence decisions using data science methodologies and tools.
Use our modern data stack to build data products and create production-quality dashboards to convey insights.
Create rules and systems to enhance the full lifecycle of risk and fraud measurement.
Analyze the user reports and behaviors that drive them on our platform.
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.
Inform the engineering, operations, and machine learning roadmaps through analysis of marketplace, user behavior, and product trends.

Requirements

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.
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 Snowflake, Big Query, Red Shift, 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.