Director of Credit Reporting and Analytics
CSS Staffing, Omaha, NE, United States
Credit Fraud Strategist with SQL/R/Python
This leadership role will help shape the credit team’s data strategy, reporting framework, and analytical capabilities. The role will work extensively to help transform complex credit data into actionable business intelligence that drives informed risk decisions and supports various business lines including consumer and small business cards. A key focus will be on leading an AI project aimed at enhancing predictive analytics and automation within the credit function.
Are you a Financial leader who is very quick, able to think on your feet; and experienced interfacing with IT, Marketing, Operations, Fraud with both technical and analytical business skills? We’d love to hear from you! Huge growth opportunity in a growing organization with our premier Omaha client. We are seeking a candidate with 8-10 years of credit card underwriting, experienced with payment holds for charging higher balances. You will eventually have your own team in future but to start current need is hands‑on. This role has lots of freedom and flexibility in work schedule if you have the drive to make things happen - drive and own the effort.
Key Responsibilities
Lead efforts to detect and manage 1st party fraud at both the point of acquisition and during portfolio management
Establish High Risk Credit Management Manual Review Team & Process
Establish a formal High Risk Credit Management Manual Review process to review risk accounts for 1st party Fraud reasons and close or give credit line decreases to these population
Ensure High Risk Credit Management Manual Review process is established under the Compliance guidelines considering regulatory requirements allowing us to use appropriate adverse actions
Create targeted ad hoc reports to identify suspicious accounts for review by the High Risk Credit Management Manual Review
Create the algorithms and pull from disparate data sources
Manage and continuously enhance payment float strategies (Open To Buy strategies) by integrating new data sources and optimizing system capabilities
Optimizing the Open to Buy strategy rules while reducing the bounced payments and charge‑offs vs. increasing customer experience through increasing lines available and float less payments for shorter time frames
Bring new tools to complement EWS coverage to increase the coverage ratio for payments
Focus on unauthorized payments and reduce the risk through closing and/or denying name mismatch DDA accounts
Manage and enhance HIL and UCL strategies for 1st and 3rd party purposes
Align the pre‑approval and post‑approval manual review population while considering the expectations from Business as well as considering the losses and funding speed of the loans
Add new tools to capture fake documents for high exposure HIL loans which are prevalent in 1st party fraud
Manage daily reviews of HIL and UCL, and the performance of the manual analysts. Complete feedback loop with the Business for collusive merchants and with HIL Risk team for the necessary underwriting strategies
Serve as Credit's primary liaison with the Enterprise Data Management (EDM) team
Act as a data steward and subject matter expert for credit‑related data within the enterprise data warehouse and Galaxy ensuring data quality, accessibility, and usability. Promote advanced analytics such as Machine learning models and Chaid analysis to be able to improve the strategies
Lead the enhancement of AI applications within Credit and Fraud to drive automation, detect anomalies, and improve operational efficiency
Contribute to the design of fraud underwriting processes within new Galaxy platform implementations
Partner with VP of IT to be able to implement new AI use cases which will help to make Fraud/Credit reporting more automated. Create tools with IVY and w/o IVY (e.g. w/ Zest) to detect payment and behavior anomalies to detect Fraud earlier
Team Leadership & Development
Required Qualifications
Credit risk underwriting (credit card) 8+ years of experience in credit analytics, risk management, or a related field
Fraud Application experience
Payment holds strategy experience and Payment float experience
SQL/ R/ Python 3 years of experience
Ideally seeking C++/Java/ SAS/ Python/ database management queries.
Must know data well - hands on EXCEL experience
Data warehousing concept, how to import from Snowflake
Statistics and business acumen - connecting the dots in a complicated credit and fraud risk domain that helps the organization make good strategic decisions
Preferred Qualifications
Master’s degree in Statistics, Data Science, Mathematics, Finance, Business or related field
Ideally seeking broad and sophisticated background of Data Science, Analytics, organizing and structuring strategy and approach. Will be individual contributor at first pulling code, analytics, identify, approve and implement while validating the backside and making business recommendations.
Breadth of experience needed, not pigeon holed – need exposure to different disciplines and teams
Additional Information
Direct Hire
Salary 130‑160K based on experience + bonus and 5K relocation
Excellent Benefits and Incentives
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Are you a Financial leader who is very quick, able to think on your feet; and experienced interfacing with IT, Marketing, Operations, Fraud with both technical and analytical business skills? We’d love to hear from you! Huge growth opportunity in a growing organization with our premier Omaha client. We are seeking a candidate with 8-10 years of credit card underwriting, experienced with payment holds for charging higher balances. You will eventually have your own team in future but to start current need is hands‑on. This role has lots of freedom and flexibility in work schedule if you have the drive to make things happen - drive and own the effort.
Key Responsibilities
Lead efforts to detect and manage 1st party fraud at both the point of acquisition and during portfolio management
Establish High Risk Credit Management Manual Review Team & Process
Establish a formal High Risk Credit Management Manual Review process to review risk accounts for 1st party Fraud reasons and close or give credit line decreases to these population
Ensure High Risk Credit Management Manual Review process is established under the Compliance guidelines considering regulatory requirements allowing us to use appropriate adverse actions
Create targeted ad hoc reports to identify suspicious accounts for review by the High Risk Credit Management Manual Review
Create the algorithms and pull from disparate data sources
Manage and continuously enhance payment float strategies (Open To Buy strategies) by integrating new data sources and optimizing system capabilities
Optimizing the Open to Buy strategy rules while reducing the bounced payments and charge‑offs vs. increasing customer experience through increasing lines available and float less payments for shorter time frames
Bring new tools to complement EWS coverage to increase the coverage ratio for payments
Focus on unauthorized payments and reduce the risk through closing and/or denying name mismatch DDA accounts
Manage and enhance HIL and UCL strategies for 1st and 3rd party purposes
Align the pre‑approval and post‑approval manual review population while considering the expectations from Business as well as considering the losses and funding speed of the loans
Add new tools to capture fake documents for high exposure HIL loans which are prevalent in 1st party fraud
Manage daily reviews of HIL and UCL, and the performance of the manual analysts. Complete feedback loop with the Business for collusive merchants and with HIL Risk team for the necessary underwriting strategies
Serve as Credit's primary liaison with the Enterprise Data Management (EDM) team
Act as a data steward and subject matter expert for credit‑related data within the enterprise data warehouse and Galaxy ensuring data quality, accessibility, and usability. Promote advanced analytics such as Machine learning models and Chaid analysis to be able to improve the strategies
Lead the enhancement of AI applications within Credit and Fraud to drive automation, detect anomalies, and improve operational efficiency
Contribute to the design of fraud underwriting processes within new Galaxy platform implementations
Partner with VP of IT to be able to implement new AI use cases which will help to make Fraud/Credit reporting more automated. Create tools with IVY and w/o IVY (e.g. w/ Zest) to detect payment and behavior anomalies to detect Fraud earlier
Team Leadership & Development
Required Qualifications
Credit risk underwriting (credit card) 8+ years of experience in credit analytics, risk management, or a related field
Fraud Application experience
Payment holds strategy experience and Payment float experience
SQL/ R/ Python 3 years of experience
Ideally seeking C++/Java/ SAS/ Python/ database management queries.
Must know data well - hands on EXCEL experience
Data warehousing concept, how to import from Snowflake
Statistics and business acumen - connecting the dots in a complicated credit and fraud risk domain that helps the organization make good strategic decisions
Preferred Qualifications
Master’s degree in Statistics, Data Science, Mathematics, Finance, Business or related field
Ideally seeking broad and sophisticated background of Data Science, Analytics, organizing and structuring strategy and approach. Will be individual contributor at first pulling code, analytics, identify, approve and implement while validating the backside and making business recommendations.
Breadth of experience needed, not pigeon holed – need exposure to different disciplines and teams
Additional Information
Direct Hire
Salary 130‑160K based on experience + bonus and 5K relocation
Excellent Benefits and Incentives
#J-18808-Ljbffr