Job Description

Role Summary In this role, you will be involved in the First Party Fraud / Credit Abuse Strategy team for a leading US bank focusing on end-to-end delivery of analysis and seamless execution by collaborating with cross-functional teams. You will get an opportunity to derive insights from large complex datasets and impact business decisions through data-based findings.  Responsibilities Design & Implement data driven First Party Fraud/ synthetic frauds strategies, credit abuse. Generate and automate regular reports and dashboards for fraud-related KPIs, offering actionable insights for Senior management. Analyze transaction data and customer behavior to identify early warning signs of fraud and proactively address vulnerabilities. Identification of potential check and deposits fraud - activity related to checks, such as forged checks, counterfeit checks Independently address complex problems and share insights from data analysis that integrate with initial hypothesis and business objective Comfortable working with large datasets, including managing large number of data sources, analyzing data quality, and pro-actively working with client's data/ IT teams to resolve issues Reformulate highly technical information into concise business presentations Create presentations and reports based on recommendations and findings  Basic Qualifications Bachelor's or master's degree in mathematics, statistics, economics, computer engineering or analytics related field  4+ years of consulting, analytics delivery experience in Fraud, Disputes, operations and exposure to credit card & retail banking domains Hands-on experience with SAS/SQL and Microsoft Office Excellent communication, presentation and story building skills  Strong analytical skills with the demonstrated ability to research and make decisions based on the day-to-day complex customer problems  Desired Qualifications Ability to adapt to emerging analytic tools and solutions into standard operating procedures Knowledge of basic machine learning algorithms like decision trees, regression models and clustering Expertise in fraud application loss mitigation on cards portfolio