Lead Fraud Data Analyst

The Lead Fraud Data Analyst is responsible for designing unique analytic approaches to detect, assess, and mitigate fraud risk. You will help analyze the effectiveness of our day-to-day fraud policy and rules in our fraud systems. You will analyze large amounts of account and transaction data to develop and improve processes, and models while managing the customer experience. This role requires fast-paced, and time-sensitive turnaround, but can be a rewarding opportunity to help fight deposit and lending fraud daily.

What you'll be doing

• Develop new data driven rules that optimize decisioning performance between member experience and fraud prevention
• Work with external fraud prevention vendors to optimize their performance and assist in maintaining, design and implementation of existing and new fraud detection tools
• Aggregate and analyze internal and external risk datasets to understand performance of fraud decisioning and granular performance of multiple decision methods
• Find insights from risk data sets to improve fraud strategies and rules
• Participate in regular review of activities involving different fraud topologies
• Able to lead fraud/risk investigations for both simple and complex cases to successful conclusions and mark false positives and false negative cases
• Complete investigation of customers and transactions flagged by the Bank's automated monitoring system(s), manual reports, and/or referrals
• Define, implement, and manage rule rationalization and governance process 
• Manage responsibilities for 3-4 junior analysts, help with prioritization, and review work

You'll bring the following required skills and experiences

• 7+ years of fraud or risk experience focused on the risk strategy and risk analytics
• Experience working at a FinTech or start-up is a plus
• 3+ years of experience in SQL queries, Tableau (and other BI tools), and day-to-day use of R/Python is a plus
• Strong knowledge of financial products, including ACH, RDC, debit cards, credit cards, lending products, and deposit accounts
• Knowledge of fraud management systems such as Falcon, RSM, JackHenry, Iovation, Threat metrics, Verafin, etc
• Deep understanding of the financial fraud space
• Experience creating and optimizing fraud prevention rules and strategies
• Knowledge of a scripting programming language such as Python
• Strong BI and analytics skills and knowledge of tools such as SQL/Athena Rule rationalization and governance 
• Coaching and mentoring other analysts
• Cross-functional stakeholder management, working with engineers, product, operations, and data science

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