Statistical Modeling Lead - Consumer-NAR

Statistical Modeling Lead - Consumer-NAR - VCI003539 - Hybrid

Primary Location

 - United States-Virginia-Reston

Description

 

Role Summary:

The Lead Statistical Modeler will lead all Consumer Credit Loss Forecasting initiatives for the North American Region portfolios (US / Canada).  Under the guidance of the Statistical Modeling Manager, the person in this role will be responsible for design, development, implementation and validation of predictive models that enable the company to efficiently and effectively manage the performance of retail and lease portfolios. This is a high visibility role that will require comfort communicating with Senior Leadership and External Auditors frequently.  The person in this role will be key in providing strategic guidance to steer management towards data-driven business decisions that impact credit policies, account management strategies, and credit loss provisioning.

Role Responsibilities:

·       Lead US and Canadian Statistical Modeling Projects – Deeply involved  in all efforts related to the design, development, governance, testing, documenting and execution of the IFRS9 impairment models for the consumer credit portfolio used for calculating expected credit loss (ECL) through probability of defaults (PD), loss given default (LGD) and exposure at default (EAD) based upon customer payment and delinquency behavior, credit profile, macro-economic trends and other portfolio and market dynamics 

·       Quarterly Audits - Support all quarterly audit related activities for both US & Canadian portfolios including, but not limited to, providing external auditors with all supporting data and documentation to ensure smooth quarter closings 

·       Support Risk Management’s efforts to collaborate with Accounting and Controlling in establishing adequate credit loss provisions for VCI’s & VCCI’s consumer and commercial credit portfolios. Conduct sensitivity tests on the models to accommodate qualitative/quantitative overlays related to the economy, used car market volatility, changes in credit policies, collection strategies and other factors that may affect future portfolio performance. Explain to Controlling executive Stakeholders variances in monthly provisions, vs. Budget and vs. Planning Round. 

·       Support and coach junior team members in enhancing technical and communication skills 

·       As needed, assist the Consumer Credit Risk Portfolio Management team in analyzing credit risk data and emerging trends to develop, test and optimize risk factors related to credit and collection policies; also assist in validating and testing the effectiveness and the impact of credit policies and new products/programs on portfolio delinquencies and losses; this includes development of new reports or modification of existing reports 

·       Represent the Risk Management consumer credit team in the Data Stewards and Resource Sub-committee. Work with the data stewards to develop data quality validation processes and partner with cross-functional users of the data warehouse to identify operational and reporting data quality issues. As needed, work with IT and the business in identifying solutions and testing data fixes 

·       Support ad hoc requests in areas related to related to consumer portfolio risk 

Qualifications

 

Experience: 

·       7+ years of professional experience, 3+ years working in a Predictive Analytics or Statistical Modeling-related role in financial services industry; experience in Risk Management a plus

Required Education:

·       Master’s Degree in a quantitative discipline: Statistics, Mathematics, Economics, Finance, Operations Research, Data Science; or, Bachelor’s degree in quantitative discipline + MBA

General Skills Needed:

·       Ability to conduct large scale projects and research through all stages: concept formulation, definition of metrics, determination of appropriate methodology, research evaluation and final research report

·       Demonstrated understanding and experience with relational datasets, data warehouses, data mining and data analysis techniques

·       Ability to effectively communicate technical subjects to business stakeholders and audience who have limited background in mathematics or statistics

·       Analytical and conceptual thinking – ability to understand business problems and develop data-driven solutions

·       Comfortable coaching junior team members in coding and statistical modeling

Specialized Skills Required:

·       Advanced knowledge of concepts, principles, standards, practices and techniques relating to statistical modeling, analysis, interpretation, and communication

·       Demonstrable strong working knowledge of SAS Enterprise Guide, ability to write/ edit macros and automate processes

·       Familiarity with other Analytics / Data Science tools: R, Stata, Python, RapidMiner, SageMaker; Experience converting SAS processes to Python a plus

·       Ability to work effectively across portfolio risk teams and functional areas teams

·       Excellent communication and presentation skills targeting a variety of audiences; experience working with external auditors a plus

·       Experience writing SQL queries and utilizing BI tools like Tableau for visualizations and reporting

·       Experience with Model Risk Management  

·       Experience with financial reporting standards (IFRS9 / IAS / CECL) and stress testing techniques

·       Familiarity with Project Management / scoping timelines for large-scale projects

·       Knowledge of US / Canadian consumer behaviors as relates to macro-economic trends

Work Flexibility:

·       Minimal travel – 5%

·       Flexibility to work remotely on as-needed basis

·       Role may require occasional/seasonal work outside of normal working hours.

Volkswagen Group of America is an Equal Opportunity Employer. We welcome and encourage

applicants from all backgrounds, and do not discriminate based on race, sex, age, disability, sexual orientation, national

origin, religion, color, gender identity/expression, marital status, veteran status, or any other characteristics protected

by applicable laws

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