Director, Predictive Modeling - Remote

Job Summary

The Director of Predictive Modeling will lead the development and implementation of predictive models and advanced analytics solutions to support key business decisions in one or more practices and/or areas across the Company, such as underwriting, collections, loss mitigation, credit pricing, marketing, customer relationship management (CRM) analytics, and/or strategic portfolio risk. The Director will manage a team of data scientists and collaborate closely with stakeholders across various departments to identify opportunities, forecast trends, and optimize performance.

Job Responsibilities

•    Develop strategic plans to create and maintain forecast models with various strategy teams across the enterprise, to identify opportunities to maximize revenues and minimize losses. Review and evaluate economic trends and incorporate those trends into forecasting models as appropriate. Defines the data requirements for capturing model characteristics across multiple environments.
•    Oversee the design, development and validation of custom application, custom behavior, and key business scorecards. Supports implementation and maintenance of scorecards across the business. Recommends new scorecards, technologies, and utilization. Provides independent scorecard validation and monitoring of scorecard effectiveness on a scheduled basis.
•    Governance & Compliance: Ensure models comply with relevant regulations, industry standards, and internal guidelines. Implement best practices for model governance, validation, and documentation.

•    Responsible for keeping the Executive Leadership team informed, by providing insights to portfolio performance and creating and leading presentations about portfolio trends; works with key business leaders to develop content of Risk and Pricing Committee, recommending changes to programs and pricing to facilitate profitable growth while managing risk within acceptable levels.
•    Partners with Retail Operations leadership to set proper collections and risk mitigation strategies, departmental targets, and incentives, as well as process analysis and design leadership, and servicing system enhancements that promote continuous improvement towards achieving company profitability and loss objectives.
•    Directs the development of predictive Loss Forecasting methodology including multiple forecasting methods to support budgeting, forecasting, reserve setting, pricing and deal structuring capabilities.
•    Review performance of implemented plans and models and the effect of expansion to other areas such as Pricing, Marketing, Protection Products, Bike Value Modeling, Commercial or Dealer Network evaluation.

Education Requirements

Education Specifications

• Bachelor’s Degree in quantitative discipline such as Statistics, Data Science, Computer Science, Mathematics, or a related field
• Master's Degree or Ph.D. Preferred

Experience Requirements

•    Typically requires a minimum of 12 years of experience in predictive modeling, data science, or advanced analytics, with at least 5 years of leadership experience
•    A performance-oriented, resourceful, and decisive individual; team player as well as team leader, with track record of leading Risk organization and cross-functional teams to successful execution with Credit/Risk/Modeling experience in financial services industry.
•    Must display exceptional leadership and professional management skills with proven ability to achieve superior results in a management role.
•    Must have a leadership style that encourages collaboration, cooperation, and teamwork with the ability to motivate others.   Can foster team collaboration through clear objectives, processes, and measurements.
•    Experienced Risk leader who knows how to evaluate consumer credit risks, and lead development and implementation of strategies for use of scoring techniques, decision rules, and tools to include strong database and/or statistical SAS programming knowledge; experience with artificial intelligence is a plus.
•    Ability to manage multiple tasks of varying difficulty, within competing and at times changing priorities
•    Preferred Skills:
•    Experience with deep learning, NLP, or AI-driven modeling.
•    Familiarity with cloud computing environments such as AWS, GCP, or Azure.