Payments Risk Analyst II

🌍 Remote, USA 💹 Full-time 🕐 Posted Recently

Job Description

Identify, investigate and mitigate fraudulent deposit activity.
Implement logic targeting bad actors and high-risk behavior associated with unauthorized deposit activity across multiple regions.
Use data to assess risk and make decisions impacting large segments of our customer population; effectively measure and assess associated tradeoffs using quantitative analysis.
Synthesize data learnings into compelling stories and communicate them to Payments Risk leadership.
Review and respond to alerts regarding anomalous customer and transaction behavior.
Measure fraud performance across payment methods, develop core metrics, and create dashboards to track and understand them.
Proactively identify and close gaps in our monitoring.
Own risk controls from end-to-end and scale processes to provide appropriate coverage as we expand our business.
Own reporting processes and communication with relevant banking partners.
Work with internal stakeholders in Product, Growth, Data Science and Engineering to develop and drive forward custom risk management strategies and techniques for different geographies.

4+ years relevant experience
2+ years fighting push payment fraud in an e-commerce or financial services environment
2+ years of experience in data analysis, working with databases, and querying (e.g., SQL, MySQL, etc).
BA / BS degree or equivalent practical experience
Strong knowledge of pull payment processes and experience with fraud claim investigations
A passion for fighting fraud and abuse, and the curiosity to self-drive investigations, identify patterns, and conduct root cause analysis
Experience in data analyses using SQL and Databricks.
Experience with data visualization and storytelling using Looker, Tableau, SuperSet or other business intelligence platforms
Be able to independently create plans for analytics projects and build collaboration within the team
Demonstrate our core cultural values: clear communication, positive energy, continuous learning, and efficient execution