Quant Analytics Associate Senior - Fraud Strategy

Job Responsibilities

• Interpret large amounts of complex data to formulate problem statement, concise conclusions regarding underlying risk dynamics, trends, and opportunities
• Manage, develop, communicate, and implement optimal fraud strategies (including rules, cutoffs, policies, operational flows, etc.) to protect the bank from fraud related losses and improve customer experience at Point of Sale
• Identify key risk indicators and metrics, develop key metrics, enhance reporting, and identify new areas of analytic focus to better capture fraud
• Provide subject matter expertise on strategy implementation/testing and initiatives related to the improvement of risk mitigation processes and infrastructure
• Collaborate with cross-functional partners to understand and address key business challenges
• Identify business opportunity by performing well thought analysis – Data mining, ensuring data integrity, synthesizing and communicating findings to senior management
• Assist team efforts in the critical development of new fraud pattern or spending pattern detection tools while providing clear/concise oral and written communication across various functions and levels, inclusive of Operations, IT, and Risk Management

Required Qualifications, Capabilities, And Skills

• Bachelor's degree (or related work experience) in a quantitative discipline in a financial services organization, plus 3 or more years' experience in fraud/risk/payments or related field
• Advanced understanding of Python, SAS, and SQL
• Ability to query large amounts of data and transform raw data into actionable management information
• Strong analytical and problem-solving abilities
• Experience delivering recommendations to management
• Self-starter with the ability to drive for resolution
• Strong communication and interpersonal skills with the ability to interact with individuals across departments/functions and with senior-level executives

Preferred Qualifications, Capabilities, And Skills

• Master's degree (or related work experience) in a quantitative discipline, preferably in a financial services organization, plus 3 or more years' experience in fraud/risk/payments or related field
• Experience with Machine Learning technologies. Knowledge of LLMs