Consumer Credit Solutions Predictive Modeler Customer Service & Call Center at Geebo

Consumer Credit Solutions Predictive Modeler

Company Name:
Wells Fargo
Position Type:
Full-time
Wells Fargo's Consumer Lending Group (CLG) is an industry leader in supporting homeowners and consumers. We put customers at the center of all that we do. We make every decision - and design every product and service - with our customers in mind.
It starts with you. We must attract, develop, retain and motivate the most talented people - those who care and who work together as partners across business units and functions. We value and promote diversity and inclusion in every aspect of our business and at every level of our organization.
The CLG team includes Home Lending, Consumer Credit Card, Personal Loans and Lines, Direct Auto, Dealer Services, Commercial Auto, Retail Services and Education Financial Services including the professional services teams that partner with these businesses - Human Resources, Finance, Credit Risk, and Compliance & Operational Risk.
Our Consumer Credit Solutions (CCS) Marketing and Channel Partnerships team supports Consumer Financial Services (credit card, personal lines and loans, direct auto loans, fee-based services and rewards programs) and Education Financial Services (EFS-student loans) with compelling cross-channel marketing and sales strategies and program execution to drive profitable growth.
Role description:
The MKTG DBASE DEC STRAT CONS (MDDSC1) will report to the DBMARS modeling manager and is responsible for developing and implementing acquisition models for Direct Auto, including:
- Develop models that optimizes a variety of unsecured credit products at a customer and portfolio level.
- Establish a proactive and engaged relationship with WF in-house counsel to communicate modeling approaches/ results for approvals.
- Engage with marketing partners to establish mutual objectives, priorities, and measures of success.
- Establish an open dialogue with risk partners.
- Continually evaluate and improve modeling and data retention processes that will increase performance and/or efficiencies.
- Develop a robust and repeatable model development process that incorporates approvals (risk, legal, compliance, and fair lending), and creates an auditable trail.
- May directly manage 1-2 technical consultants/analysts.
Basic Qualifications
8
years marketing analytics and predictive modeling experience.
Minimum Qualifications
- Financial institution background in statistical analytics within credit risk/marketing.
- Acquisition experience in unsecured lending such as Credit Card, Auto, personal lines and loans and EFS; Knowledge in areas such as direct mail targeting and balance transfer analysis.
- Ability to translate business objectives into actionable work flows in terms of data manipulation, variable selection, segmentation and optimization.
- Ability to create clear and succinct documentations on modeling methodologies and results for audit trails.
- Clear written/verbal communication and presentation skills to communicate to diverse groups of leaders including legal, compliance, risk and fair lending.
- Expert level understanding of credit marketing and risk.
- Expert level programmer in SAS and at least two other languages.
- Experience in evaluating and implementing models/strategies in big data and multiple data source environment.
Preferred Skills
Master's or higher degree in quantitative field such as Statistics, Math, Finance, etc.Estimated Salary: $20 to $28 per hour based on qualifications.

Don't Be a Victim of Fraud

  • Electronic Scams
  • Home-based jobs
  • Fake Rentals
  • Bad Buyers
  • Non-Existent Merchandise
  • Secondhand Items
  • More...

Don't Be Fooled

The fraudster will send a check to the victim who has accepted a job. The check can be for multiple reasons such as signing bonus, supplies, etc. The victim will be instructed to deposit the check and use the money for any of these reasons and then instructed to send the remaining funds to the fraudster. The check will bounce and the victim is left responsible.