Data Scientist, Risk & Fraud

Whatnot•Los Angeles, CA

2d•Remote

About The Position

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: 🔍Generate Insights & Shape Direction Translate complex data into actionable recommendations for the Fraud engineering and operations teams. Define and own the KPIs that measure the cost of fraud, strategies to prevent it, and impact to users and marketplace performance. Analyze the effectiveness of existing methods and partner with product and machine learning engineers to develop better anti-fraud practices. 🧪Drive Experimentation & Measurement Partner with product managers, engineers, and operations teams to design, implement, and evaluate feature rollouts to combat bad actors on the platform. Define and own the experimentation playbook for Fraud at Whatnot. Develop frameworks for causal inference and impact measurement of efforts that are not well-suited to A/B testing. Ensure Whatnot’s internal KPIs treat fraudulent actors appropriately in measurement outside of fraud domains. 🛠 Build Data Products & Tools Use our modern data stack to build dashboards, data pipelines, and self-serve tools that empower teams across Whatnot. Partner with engineers to improve data accessibility, ensure data quality, and support instrumentation for new product and platform enhancements. 🤝Lead Cross-Functional Collaboration Advocate for data-driven decision-making and foster a culture of measurement across the trust & risk organization. Communicate insights clearly to both technical and non-technical audiences, influencing roadmaps and strategic decisions. Bring data support to company-critical investigations to quantify and thwart bad actor tactics, and help generalize outputs to create longer-term protections for different fraud vectors. Serve as a thought leader to Trust & Risk leadership, shaping how we build, launch, and iterate on fraud strategy across the platform. US Based: 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, Seattle, Los Angeles, and San Francisco hubs.

Requirements

5+ years of experience in the Data field, and 3+ years of experience in Data Analytics & Science supporting anti-fraud, risk, trust & safety, or integrity problems.
Bachelor’s degree in Computer Science, Economics, Statistics, Cybersecurity, 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.
Advanced SQL skills and experience with modern data warehouses (Snowflake, BigQuery, Redshift) and tools like Spark or DBT.
Proficiency with Python or R for data analysis, modeling, and experimentation.
Experience designing and analyzing A/B tests and understanding causal inference techniques.
Strong data visualization skills and familiarity with BI tools for building interactive dashboards.
Ability to communicate complex ideas clearly, concisely, and impactfully across diverse stakeholders.
Experience leading cross-functional projects and influencing trust & risk strategy with data.
Comfortable working in fast-paced, ambiguous environments with a high degree of ownership.

Responsibilities

Translate complex data into actionable recommendations for the Fraud engineering and operations teams.
Define and own the KPIs that measure the cost of fraud, strategies to prevent it, and impact to users and marketplace performance.
Analyze the effectiveness of existing methods and partner with product and machine learning engineers to develop better anti-fraud practices.
Partner with product managers, engineers, and operations teams to design, implement, and evaluate feature rollouts to combat bad actors on the platform.
Define and own the experimentation playbook for Fraud at Whatnot.
Develop frameworks for causal inference and impact measurement of efforts that are not well-suited to A/B testing.
Ensure Whatnot’s internal KPIs treat fraudulent actors appropriately in measurement outside of fraud domains.
Use our modern data stack to build dashboards, data pipelines, and self-serve tools that empower teams across Whatnot.
Partner with engineers to improve data accessibility, ensure data quality, and support instrumentation for new product and platform enhancements.
Advocate for data-driven decision-making and foster a culture of measurement across the trust & risk organization.
Communicate insights clearly to both technical and non-technical audiences, influencing roadmaps and strategic decisions.
Bring data support to company-critical investigations to quantify and thwart bad actor tactics, and help generalize outputs to create longer-term protections for different fraud vectors.
Serve as a thought leader to Trust & Risk leadership, shaping how we build, launch, and iterate on fraud strategy across the platform.

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
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.