Modeling - Risk Modeler I

CreditOne; Las Vegas (NV); On-site; USD 90,000 - 120,000; Full time

Job summary

Credit One Bank is seeking a Risk Modeler I to join the Risk Management team in Las Vegas. The role focuses on developing, implementing, and monitoring consumer credit risk and decision models for customer acquisition, underwriting, and collections.

The candidate will work under guidance to build machine learning solutions, handle data extractions, feature engineering and model governance, with a preference for demonstrated ML implementations and credit experience.

Qualifications

• Bachelor’s degree in quantitative discipline; internships/academic projects may demonstrate skills.
• 2+ years of relevant modeling or quantitative analytics experience.
• Proficiency in Python/PySpark and SQL for data preparation, analysis and reproducible coding.
• Understanding of predictive modeling, training/evaluation datasets, overfitting and ROC/AUC/KS measures.
• Ability to identify data inconsistencies, question results and document assumptions.
• Clear written and verbal communication; ability to deliver defined tasks under guidance.

Responsibilities

• Support development of risk models and consumer decision models; fit machine learning models.
• Extract/join consumer data using SQL and Python; reconcile counts and document data assumptions.
• Perform exploratory analyses and feature engineering for segmentation.
• Train/test splits and monitor for data leakage; assess model performance measures.
• Analyze score distributions and portfolio/vintage performance for changes.
• Maintain reproducible code and model development documentation; QA checks.
• Prepare exhibits and respond to Model Risk Management questions.
• Collaborate with Model Operations and Technology to support testing and score reconciliation.
• Communicate findings and limitations clearly to modelers and business partners.

Skills

Python/PySpark

SQL

Data analysis

Modeling concepts (ROC/KS)

Communication

Education

Bachelor’s degree in quantitative discipline

Master’s degree in quantitative discipline

Tools

Git

Snowflake

BigQuery

Job description

Description

Position Summary

This position is a part of the Risk Management department. Our risk professionals are tasked with identifying revenue opportunities and risk trends by monitoring the performance of our existing portfolio, growth opportunities, and general market trends. We develop and implement strategies for credit line increases, authorizations, collection, fraud, retention, and the sale of revenue-based products.

The Risk Modeler I supports the development, implementation, monitoring and maintenance of consumer credit risk and decision models within Risk Management. The role focuses on individual borrowers and credit card accounts, including customer acquisition, underwriting, credit line management and collections. Working under the guidance of experienced modelers, the employee completes defined analytical tasks and builds statistical modeling, consumer-credit and model-governance capabilities. The ideal candidate will have an outstanding track record of successfully implementing machine learning solutions for impactful and dynamic business results.

Summary of Essential Job Functions

• Support development of application and behavioral risk models and related consumer decision models. Fit machine learning and benchmark models and assist with challenger models development under technical guidance.
• Extract and join consumer application, credit bureau, account, payment and transaction data using SQL and Python/Pyspark. Reconcile record counts, investigate missing or inconsistent values and document data assumptions.
• Perform exploratory, univariate and bivariate analysis; support feature engineering and segmentation using attributes such as utilization, balances, repayment behavior and credit history.
• Apply established sampling, observation and performance windows and train/test or out-of-time splits. Identify potential data leakage and elevate unexpected results.
• Calculate and interpret model-performance measures, including KS, ROC/AUC, lift, population stability and calibration, with guidance on business interpretation.
• Execute periodic monitoring and analyze changes in score distributions, delinquency, default and charge-off performance. Support portfolio and vintage analysis and investigate material changes.
• Perform analytical QA and coding checks; maintain reproducible code, data definitions and records of development decisions.
• Prepare analytical exhibits and assigned sections of model-development documentation. Support responses to independent Validation and Model Risk Management questions.
• Partner with Model Operations and Technology to support implementation testing, reconcile development and production scores and document discrepancies.
• Communicate findings, assumptions, limitations and progress clearly to modelers and business partners; elevate data issues and delivery risks promptly.
• Follow established data-security, coding and model-governance procedures. Support model explanations and reason-code analysis where applicable under guidance from senior modelers and Compliance.
• Perform other related duties as assigned.

Position Requirements

• Bachelor’s degree in quantitative discipline such as Statistics, Mathematics, Applied Mathematics, Data Science, Computer Science, Economics, Financial Engineering, Quantitative Finance, Operations Research, Industrial Engineering, Business Analytics.
• 2+ years of relevant modeling or quantitative analytics experience. Internships, research and substantive academic projects may demonstrate the required skills.
• Working proficiency in Python/Pyspark, demonstrated through practical data preparation, analysis and reproducible coding. Ability to learn the team’s modeling tools
• Basic understanding of predictive modeling, training and evaluation datasets, overfitting and model-performance measures such as ROC/AUC and KS.
• Ability to identify data inconsistencies, question unexpected results and document analytical assumptions and limitations.
• Clear written and verbal communication, attention to detail and ability to complete defined deliverables under guidance

Preferred

• Experience or project exposure in consumer lending, credit card issuing or consumer credit bureau data.
• Hands-on exposure to machine-learning methods such as gradient boosting/XGBoost or model-explainability techniques.
• Experience with Git or another version-control tool and reusable analytical workflows.
• Exposure to Snowflake, BigQuery or another cloud analytical environment.
• Exposure to model monitoring, implementation testing or model-development documentation.
• Master’s degree in quantitative discipline such as Statistics, Mathematics, Applied Mathematics, Data Science, Computer Science, Economics, Financial Engineering, Quantitative Finance, Operations Research, Industrial Engineering, Business Analytics.

Credit One Bank, N.A. is a data-driven financial services company based in Las Vegas. Founded in 1984, Credit One Bank offers a spectrum of credit card products for people in all stages of financial life. Credit One Bank is an equal opportunity employer committed to diversity and inclusion and does not discriminate against any employee or applicant for employment because of age, race, religion, color, disability, sex, sexual orientation, or national origin. Reasonable accommodations can be made for those who require them, including access to job applications and workplace accommodations. Employment at Credit One Bank is based on mutual consent (also known as at-will). Credit One Bank does not accept unsolicited resumes from agencies and is not responsible for related fees.