Fraud Prevention Strategy - Data Analytics Associate

Analyze complex data to define fraud risk dynamics, trends, and optimization opportunities.

Develop and manage fraud prevention strategies across the credit card fraud lifecycle to reduce losses and improve customer experience.

Collaborate cross-functionally and present insights to leadership, leveraging advanced analytics and large language models to drive scalable business improvements.

Bachelor’s degree in engineering, statistics, mathematics, or related quantitative field OR 3+ years of risk management or quantitative experience.

Proficiency in Python, SAS, and SQL with ability to query large datasets and derive actionable recommendations.

Experience delivering analytical recommendations to leadership.

Work Experience Required: Minimum 3 years risk management or quantitative experience; no explicit notice period mentioned.

Experienced with applying advanced analytics and mathematical techniques to business problems in fraud prevention or risk management.

Skilled communicator able to translate complex analysis into business insights and influence senior stakeholders.

Familiarity or interest in modern data technologies and AI tools such as AWS, Snowflake, machine learning, and large language models.