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About the role
- Sr. Model Development Analyst at Santander responsible for developing credit scoring models for nonprime auto lending. Engaging in data analysis and reporting to enhance decision-making.
Responsibilities
- Responsible for developing and maintaining sophisticated empirical models - including credit scoring models - for a nonprime auto lender
- Takes a "hands on" approach to data analysis
- Summarizes and reports information to a variety of internal and external clients
- Interacts with many other departments in achieving overall company objectives
- Participates in the construction of complex mathematical models – including credit origination, pricing and customer behavior scorecards
- Develops the underlying assumptions, theory, empirical evidence, and conceptual soundness of statistical and mathematical models
- Applies statistical techniques to analyze trends and uncover risks and opportunities relative to portfolio management and originations
- Uses internal and external data sources to create robust model development datasets
- Ensures modeling projects are conducted in accordance with established company policies and generally accepted mathematical modeling practices
- Identifies modeling needs and communicates them to the Director Quantitative Modeling
- Assesses department procedures for accuracy and completeness
- Utilizes data mining and statistical techniques to develop analytic insights, sound hypotheses, and informed recommendations
- Encapsulates analytic findings into executive-level summary documents to support senior management decision-making
- Liaises with IT and other internal teams to define requirements and ensure the timely and accurate delivery of data elements for analytic projects
- Provides requisite information in support of independent model validation activities
- Develops a thorough understanding of the firm’s operations and business practices
- Supports Model Validation and documentation of models in accordance with Santander Consumer USA internal policies and U.S. Federal Reserve regulations (SR 11-07)
Requirements
- Bachelor's Degree or equivalent work experience: Statistics, Economics, Operations Research, Applied Mathematics, or a related quantitative discipline required or equivalent experience
- Master's Degree: Statistics, Economics, Operations Research, Applied Mathematics, or a related quantitative discipline required or equivalent experience. - Preferred
- Ph.D.: Statistics, Economics, Operations Research, Applied Mathematics, or a related quantitative discipline required or equivalent experience - Preferred
- 3+ Years Analytics in Financial Services Industry. - Required
- 3+ Years Indirect subprime Auto Financial Services Industry experience. - Preferred
- 3+ Years Prior experience developing credit scoring models preferred. - Preferred
- Familiarity with logistic regression models, segmentation and variable reduction techniques, hypothesis testing, neural networks, design of experiments, ANOVA, decision trees, and linear regression
- Prior experience working with credit bureau data strongly preferred
- Prior experience using Python, or SAS, SQL
- Demonstrated ability to use SQL and Python to extract data from multiple data sources
- Demonstrated ability to merge, concatenate, and prepare extremely large datasets for statistical analysis and mathematical model development
- Demonstrated ability to create complex pivot tables in MS Excel
- Ability to effectively explain advanced mathematical concepts, techniques, and analyses to a business audience
- Ability to translate analysis into a clear business plan
Benefits
- Competitive rewards package
- Health insurance
- Retirement plans
- Professional development opportunities
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