I am working with a consumer lending firm looking for a Data Scientist who will use modeling/statistical techniques to develop and maintain credit and collection risk, pricing, fraud models, and analytical strategies that will optimize business decisions across channels. In this role you will collaborate with data scientists and other teams by performing controlled experiments, including AB testing, model building, and monitoring and will have the opportunity to independently contribute to a wide range of data science projects. Candidates should have 3+ years of relevant experience with SQL, Python, or SAS - AWS experience preferred.
Responsibilities:
- Uses modeling/statistical techniques, data processing skills, and tools such as SQL, python, AWS, and SAS to develop and maintain credit and collection risk, pricing, fraud models, and analytical strategies that will optimize business (i.e. lending, contact, etc) decisions across channels.
- Maintains up-to-date model inventory and chronology of model prediction via visualization tools such as PBI.
- Designs, executes, and analyzes controlled experiments to continuously evaluate new opportunities to improve volume/credit risk trade-offs.
- Collaborates with partners across the organization in cross-functional projects to execute on key business priorities.
- Organizes data reports and communicates findings to technical and non-technical audiences.
- Evaluates, recommends, and champions new modeling data, tools, techniques, and approaches.
- Conducts evaluation of tools in AWS ML end-to-end pipeline environment.
- Conducts evaluation of new statistical approaches and enhanced segmentation to optimize approval and advance assignment strategies.
Requirements:
- Degree in quantitative field (Masters preferred)
- 1-2+ years of professional experience (preferably in financial services)
- 3+ years of experience in SQL, Python, or SAS
- Advanced abilities to handle large datasets
- Good team player, strong communication skills
