Fraud Risk Analyst II

hace 4 semanas


San José, San José, Costa Rica Amazon Support Services Costa Rica SRL A tiempo completo

Overview:
Have you ever considered the intricacies involved in identifying and mitigating fraudulent activities across a vast array of eCommerce transactions globally? What strategies would you implement to enhance trust within an online marketplace bustling with millions of buyers and sellers? How would you develop systems that adapt over time to proactively recognize and counteract new and evolving fraud threats?

Mission:
Our objective in Buyer Risk Prevention (BRP) is to establish Amazon as the most secure platform for online transactions. BRP protects every financial interaction across all Amazon platforms while ensuring that these protective measures remain seamless for our legitimate customers.

Key Responsibilities:
The Risk Analyst II will be responsible for conducting in-depth analyses to uncover emerging fraud trends and effectively communicate insights in writing to machine learning teams, program managers, and other cross-functional collaborators.

The ideal candidate will showcase the ability to excel in a dynamic and demanding environment while collaborating with stakeholders across various domains.

A Day in the Life:

  • Deliver timely analytics to partner teams
  • Conduct deep dives into emerging fraud patterns utilizing SQL on extensive datasets
  • Identify root causes and collaborate with partners, including machine learning teams, to create sustainable solutions
  • Automate processes through the use of dashboards

Basic Qualifications:

  • Proven experience in defining requirements and leveraging data and metrics to derive business insights
  • Advanced proficiency in SQL
  • Bachelor's degree or equivalent experience
  • 4+ years of experience in an analytical role
  • Familiarity with reporting and data visualization tools such as Quicksight, Tableau, Power BI, or other BI software

Preferred Qualifications:

  • Experience in scripting for automation (e.g., Python, Perl, Ruby)
  • Background in statistical analysis
  • Experience with machine learning techniques

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