Credit Risk Modeler
What you'll be doing:
- You'll develop, test, and enhance a broad variety of predictive models that will be used across all aspects of the customer life cycle including new customer acquisition, underwriting, loan offers, collections, retention and profitability analysis
- You'll collaborate with peers from other teams to create strategies for implementation of predictive models such as selection criteria for direct mail offers, approve vs. decline cutoffs and verification requirements for various customer segments, and risk-based loan offer terms (approval amount, rate and duration)
- You'll design multivariate tests to ensure continuous optimization of all aspects of the customer lifecycle
- You'll complete ad hoc analysis as needed to help management understand and interpret business results
What you'll bring:
- Bachelor's degree required in quantitative discipline (e.g., mathematics, statistics, operations research, quantitative finance, physics, engineering, computer science, etc.)
- Familiarity with (and ideally proficient in) SQL and Python or R
- The curiosity to understand how things currently work and the dedication to figure out how they should work
- Passion for keeping skills up to date and explore new methodologies
- You are a believer in teamwork that enjoys owning your part
- You are an active listener and deliver information clearly.
- You are able to identify problems and ask insightful follow up questions or escalate issues when necessary.
- You make an impact and deliver on assigned tasks through effectively using resources around you and by taking advantage of learning opportunities.
- You care about your work and value gaining credibility by meeting goals and deadlines.
- You value sharing information and seek input from others.
- You’re comfortable with the unknown: startup life allows for a broad, wide ranging role, but also means priorities and work can change quickly. You should be excited about all this entails!
What makes you stand out:
- Advanced degree in a relevant field or 2 years of relevant work experience
- Experience with a financial services company
- Experience in any of the following machine learning models: linear, non-linear, and ensemble