Senior Decision Science Manager

Position Overview:

Car Capital Technologies is seeking a Senior Decision Science Manager who will work with the Chief Risk Officer on managing current and building future generations of scoring models to help manage the business. If you have a proven track record of leveraging data to build statistical models to help the business make better decisions then we want to discuss this exciting opportunity to build something great here at Car Capital.

As the Senior Manager, you’ll own the scoring model performance and creation for managing the risk dynamics of portfolio acquisition and performance. You will be responsible for driving end-to-end product life cycle scoring and decision making. This position will leverage existing data and technological infrastructure to deliver near real time decisions available for enterprise consumption and analysis.  The role will also be the key driver of exploration on new data sources and possible integration with decision making processes. 

This role has a competitive comp and benefits structure.

Education & Experience:

Minimum Qualifications

You are a motivated, proactive Data Modeler with a clear idea of how to do consumer loan portfolio score development, monitoring and analysis.  In addition, you possess the following:

• 8-10 years of consumer credit risk score model development experience, or equivalent
• Proficient with various analytical tools (querying data warehouses, consolidating data, cleaning up and delivering presentation layer tools that permit detailed analysis)
• High-degree of technical knowledge. Experience with various data modeling tools (SAS, R, Tableau, Python, etc.) a definite must.
• Experience translating business issues or data abnormalities through unique or novel model features to drive best decisions
• Capable of quickly establishing and fostering partnerships with other stakeholders in a highly cross-functional, matrixed organization
• Strong user experience sensibilities and familiarity with statistical tool best practices
• Masters degree in Mathematics, Statistics, Computer Science, Decision Science, or other related field, training or equivalent work experience
• Strong self-starter that needs very little direction once business issue is identified and scoped

Preferred Qualifications

• Working knowledge of Indirect Auto Lending, FinTech, and / or Consumer Lending
• Experience working with start-up organizations
• Knowledge of Azure and SaaS-based technology platforms and systems

Skills:

• Statistical expertise able to develop and document scoring models for approval and deployment
• Excellent data analysis and querying skills
• Attention to detail and intellectually curious
• Excellent organizational skills
• High degree of emotional intelligence
• Service-oriented
• Ability to think critically
• Excellent problem solving skills

Responsibilities:

• Set the Scoring Model vision for Risk Management
• Build the roadmap to facilitate creation and update of Scoring models to be used by the Organization
• Quickly learn and adapt to technological tools available currently and best leverage those to deliver on the above
• Identify adverse trends seen in the data and effectively identify solutions through scoring platform and communicate recommendations to leadership
• Participate as a member of Risk Scoring community in setting future model upgrades and innovations

About Car Capital Technologies:

• Car Capital Technologies was founded to provide dealers with capital and advanced technology to help all consumers buy the cars they need.
• With 100% automated instant approvals available to our dealer partners, we make it possible for any driver regardless of credit history to feel confident in the ability to purchase a vehicle when entering the dealership.
• Every dealer partner has access to our proprietary web-based platform, Dealer Electronic Auto Loan System (DEALS). DEALS provides the tools for our dealer partners to do what they do best, sell cars. Our program allows our network of dealers to make their own approval decisions based on the economics of each unique car and consumer.