Quant Analytics Associate I - Fraud Strategy
- 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
- Bachelor's degree (or related work experience) in a quantitative discipline in a financial services organization, plus 2 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.
- Master's degree (or related work experience) in a quantitative discipline, preferably in a financial services organization, plus 2 or more years' experience in fraud/risk/payments or related field.
- Experience with Machine Learning technologies and knowledge of LLMs.
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