Fraud Prevention Strategy - Data Analytics Associate

Make your mark protecting customers by turning complex analytics into fraud strategy, with strong growth and mobility opportunities.

As a Quantitative Analytics Associate within the Fraud Prevention Optimization Strategy team, you reduce fraud losses and operating expenses while balancing customer impact by optimizing business processes and decisioning. You deliver complex analyses paired with business insight, collaborate with cross-functional partners, and present conclusions succinctly to managers and executives. You leverage advanced analytics and tools such as large language models to drive sustainable, scalable business improvements.

Job Responsibilities:

• Interpret complex data to define problem statements and deliver concise conclusions on risk dynamics, trends, and opportunities
• Apply advanced analytical and mathematical techniques to solve complex business problems
• Develop, communicate, implement, and manage fraud strategies to reduce fraud-related losses and improve customer experience across the credit card fraud lifecycle
• Identify key risk indicators, define and enhance key metrics and reporting, and uncover new areas of analytic focus to challenge current business practices
• Provide data insights and performance updates to business partners
• Collaborate with cross-functional partners to solve key business challenges
• Support critical projects with clear, concise verbal and written communication across functions and levels
• Champion the use of modern technology and tools, such as large language models, to drive value at scale

Required qualifications, skills, and capabilities:

• Bachelor's degree in engineering, statistics, mathematics, or another quantitative field, or 3+ years of risk management or other quantitative experience
• Proficiency in Python, SAS, and SQL
• Ability to query large datasets and translate results into actionable recommendations
• Strong analytical and problem-solving skills
• Experience delivering recommendations to leadership
• Self-starter with the ability to execute quickly and effectively
• Strong communication and interpersonal skills, with the ability to partner across departments and functions and with senior-level leaders

Preferred qualifications, skills, and capabilities:

• Master's degree in a quantitative field, or 4+ years of risk management or other quantitative experience
• Hands-on knowledge of Amazon Web Services and Snowflake
• Experience applying machine learning, large language model prompting, or natural language processing techniques