Payments Fraud & Monetization Risk Analyst

Jobtailor; California (MO); On-site; USD 90,000 - 140,000; Full time

Job summary

Jobtailor is seeking a detail-oriented fraud risk analyst to investigate complex payment fraud, monetization abuse, and account misuse across ecommerce ecosystems. You will analyze transactions, subscription usage, and customer behavior to identify patterns and recommend mitigations.

Collaborate with Data Science, Engineering, and Product teams to define abuse indicators, improve model performance, and implement scalable detection and enforcement controls.

Qualifications

• Exceptional written and verbal communication skills.
• Strong analytical and investigative capabilities.
• Experience with SQL and data analysis techniques.
• Understanding of payment and commerce risk concepts (chargebacks, account abuse, fraud).
• Ability to translate findings into scalable detection or enforcement strategies.
• Creative problem-solving and ability to work under ambiguity.

Responsibilities

• Investigate complex payment fraud and monetization abuse across ecommerce ecosystems.
• Analyze transactions, subscription usage, and customer behaviors to identify patterns.
• Develop risk assessments, recommendations, and enforcement actions.
• Collaborate with Data Science, Engineering, and Product teams to define abuse indicators.
• Translate findings into scalable detection strategies and operational controls.
• Balance fraud prevention with customer experience in controls and policies.

Skills

SQL Data Analysis

Fraud Investigation

Risk Assessment

Analytical Thinking

Adaptability

Communication

Attention to Detail

Tools

Analytical Tools

AI-Driven Detection

Machine Learning Concepts

Job description

Jobtailor is seeking a detail-oriented fraud risk analyst to investigate complex payment fraud, monetization abuse, and account misuse across ecommerce ecosystems. You will analyze transactions, subscription usage, and customer behavior to identify patterns and recommend mitigations.

Collaborate with Data Science, Engineering, and Product teams to define abuse indicators, improve model performance, and implement scalable detection and enforcement controls.