Super.com is looking to proactively detect and prevent earnings fraud by analyzing user and transaction-level data, building fraud monitoring dashboards, investigating suspicious activity, and collaborating with cross-functional stakeholders to develop and improve fraud rules.
Requirements
- Advanced proficiency in SQL for data analysis, including building production-ready queries, data models, and analytics workflows
- Experience working with complex datasets in modern data warehouse and transformation tools (e.g., Snowflake, dbt) and at least one BI/dashboarding tool (e.g. Looker, Amplitude, Tableau)
- Comfortability working with messy, event-level product data across mobile and web, tracing end-to-end user journeys and debugging tracking or data quality issues with engineers
Responsibilities
- Analyzing complex datasets to identify patterns of fraudulent behaviour and generate actionable insights that reduce earnings fraud
- Building and maintaining dashboards and reporting solutions for fraud monitoring and self-serve analytics
- Collaborating with cross-functional teams to scope, prioritize, and deliver analytics projects that inform product roadmap and operational decisions
- Investigating anomalies and support root cause analysis for fraud incidents
- Supporting development and testing of fraud rules: help define objectives, use data to inform and optimize rules, analyze A/B tests, and measure impact on key metrics
- Advocating for data quality, governance, and best practices to ensure consistency, reliability, and security of our data assets
- Communicating findings clearly to technical and non-technical stakeholders across written and verbal channels, while documenting processes and results
Other
- 5+ years of experience in data or analytics roles. Bonus if part of that time was spent in fraud, risk, trust & safety, or credit/collections domains
- Proven track record of owning ambiguous problem spaces end-to-end: scoping questions, defining clear success metrics, validating data quality, and iterating based on stakeholder feedback
- Excellent communication and data storytelling skills, able to translate complex analysis into clear, actionable recommendations for both technical and non-technical audiences
- Demonstrated ability to partner cross-functionally including prioritizing competing requests and aligning stakeholders on next steps and trade-offs
- High degree of ownership and urgency, comfortable working in a fast-paced environment where fraud patterns evolve quickly and decisions have meaningful financial impact
