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Senior Manager, Credit Risk Analytics
US Cellular
Chicago, IL, United States
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As part of the Finance department, the U.S. Cellular® Shared Analytical and Data Services team (SANDS) is comprised of highly technical resources with diverse backgrounds in data warehousing, business intelligence, analytics, forecasting, and CRM. The SANDS team is responsible for delivering data solutions, analytical tools, campaign execution, and business insights to our stakeholders in Marketing, Finance, Customer Service, and Sales Operations. The Sr. Manager – Credit Risk Analytics leads a team or multiple teams of high performing technical/analytic resources, which guide the creation and operationalization of models and other advanced analytics for Finance, Customer Service, Marketing, Sales Operations and Engineering. This role will provide the strategy and lead the development and execution of advanced modeling techniques for managing Credit Risk, CRM initiatives, and statistical forecasting. This role fosters innovation and provides technical and professional expertise in analytical methods and tools to introduce new methods and tools to the organization.
• Evaluate and enhance modeling process (requirements, design, delivery and measurement (accuracy rate and ROI) of predicted models, forecasts and other statistical techniques. These outputs can be leveraged in a variety of ways.
◦ Data Mining Models – Prediction of customer behavior i.e. voluntary and involuntary churn, portfolio management and performance of credit policies, fraud prevention, etc.
◦ Segmentation Models – customer/geographic and credit risk segmentation identification i.e. store/market segments.
◦ Forecasts – Prediction of enterprise metrics i.e. defects, gross ads, non-pay disconnects, first-payment default rates etc.…
◦ Performance of scenario based outcomes and predications i.e. device price elasticity, predicted impact of device price change, impact on loss rates based on credit scoring and deposit assignments.
• Develop analytically based recommendations to improve credit risk evaluation of new and existing customers; leverage analytics to predict promotions and sales channel profitability, customer behavior risk scoring, lifetime value, survival rates as well as other standard credit risk measures / analyses.
• Provides analytical leadership for the continuous evolution of credit risk strategy to align with our business objectives
• Responsible for model development and execution redesign and model measurement.
• Manage stakeholders from the VP level down to peers.
• Provides strategic direction on how analytics can be used; ability to sells new analytic concepts and projects to their stakeholders.
• Manage client escalations.
• Develop long-term strategic vision for Credit Risk and CRM infrastructure including model execution and measurement in batch and real-time channels.
• Ph.D. or Ph.D. candidates (ABD) Statistics, Economics or other quantitative field or Masters in Statistics, Economics or other quantitative field with additional relevant work experience required.
• 12+ years of relevant experience in multiple analytical techniques and data manipulation leveraging SAS or other similar statistical tools required.
• 7+ years leading, training and coaching a technical team and /or business resources required.
• Significant knowledge in leveraging analytical techniques in Marketing, Finance, Credit Risk, Fraud or Receivables required.
• Excellent skills with SQL and SAS required.
• Excellent knowledge leveraging financial techniques i.e. ROI, NPV and Cash flow to measurement the effectiveness of the teams output required.
• Excellent ability to translate analytical findings into actionable customer strategies which will have a measured impact on the business required.
• Excellent organization and project management skills required.
• Proven ability to meet tight timelines, estimate level of effort, multi-task and prioritize workload required.
• Strong communication skills, including written and verbal, including formal presentations required.
• Ability to review current analytical and data processes to improve for efficiencies required.