Hybrid Fraud Risk Strategy Lead

Early Warning; Scottsdale (AZ); Hybrid; USD 84,000 - 126,000; Full time

Benefits offered by this job

Healthcare Coverage

401(k) Retirement Plan

Paid Time Off

Paid Parental Leave

Maven Family Planning

Job summary

Early Warning is seeking a Fraud Risk Analyst in the United States to analyze, deploy rules and decisioning logic for fraud risk models, working with internal teams and financial institutions to optimize risk, cost, and experience across multiple operating environments.

The role collaborates with Product, Engineering, and Analytics to align fraud strategies with program objectives and roadmap priorities, with a strong emphasis on data-driven decisioning and stakeholder communications.

Qualifications

• Bachelor’s degree or equivalent education/experience required.
• Minimum 5 years of fraud risk management experience.
• SQL/SAS/Python competencies or comparable data analytics skills.
• Proven track record of converting concepts to analysis to cases for change.
• Strong written, verbal, and presentation skills for technical and non-technical audiences.
• Experience with digital banking, payments and/or payment networks.
• Experience with financial institutions in fraud risk management.

Responsibilities

• Assess, recommend and modify scores and rules to balance risk, expense and experience for clients and partners.
• Implement fraud detection and prevention strategies for the network.
• Create data‑driven insights for monitoring and improving fraud risk strategy performance.
• Deliver clear communications to internal and external stakeholders regarding risks and trends.
• Analyze fraud events to identify root causes and mitigation opportunities.
• Monitor fraud trends and propose rule and control enhancements.
• Support protection of systems and data integrity.

Skills

SQL

SAS

Python

Education

Bachelor's degree or equivalent

Job description

Early Warning is seeking a Fraud Risk Analyst in the United States to analyze, deploy rules and decisioning logic for fraud risk models, working with internal teams and financial institutions to optimize risk, cost, and experience across multiple operating environments.

The role collaborates with Product, Engineering, and Analytics to align fraud strategies with program objectives and roadmap priorities, with a strong emphasis on data-driven decisioning and stakeholder communications.