Credit Risk Data Scientist II

United StatesRemoteFull-timemid

Data & AIRisk Data Scientist

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Quick Summary

Overview

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Credit Risk Data Scientist II based in United States.

Technical Tools

Data & AIRisk Data Scientist

This role is responsible for overseeing the development, validation, and ongoing performance of credit risk models supporting consumer lending portfolios.
You will help ensure models used for credit decisions, loss forecasting, risk management, and regulatory reporting remain accurate, effective, and well governed.
The position combines advanced data science and statistical modeling with deep knowledge of consumer credit and regulatory expectations.
You will work across model development, validation, monitoring, governance, stress testing, and CECL-related activities.
The role offers significant exposure to machine learning, econometric techniques, macroeconomic scenarios, and credit risk frameworks.
You will partner with senior leaders, auditors, regulators, and cross-functional teams to communicate model performance and risk implications.
This fully remote opportunity is ideal for an experienced quantitative professional who can combine technical depth with strong business judgment.

Oversee the development, validation, and performance monitoring of credit risk models used for credit decisioning, risk management, loss forecasting, and regulatory reporting.
Apply advanced statistical, econometric, machine learning, and data science techniques to credit risk modeling and forecasting challenges.
Support and oversee models based on Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) frameworks.
Manage and strengthen the model governance framework, ensuring appropriate controls across model development, production, validation, and ongoing monitoring.
Evaluate model performance through back testing, monitoring, validation, and adjustment activities, identifying issues and recommending appropriate remediation.
Support CECL processes, including quantitative and qualitative modeling components, implementation, production, and ongoing performance assessment.
Contribute to stress testing, credit loss forecasting, and macroeconomic scenario analysis, including applicable regulatory requirements.
Ensure model inputs and outputs meet appropriate standards for data integrity, data quality, and reliability.
Develop and implement policies, procedures, and monitoring practices for credit risk model validation and oversight.
Provide oversight of credit risk models developed or operated by external partners where applicable.
Ensure modeling and data science activities remain aligned with organizational risk appetite, strategic objectives, and regulatory expectations.
Lead or support project planning, execution, reporting, issue resolution, and follow-up action plans.
Communicate model performance, risks, findings, and recommendations to senior management, auditors, regulators, and other stakeholders.

Requirements

~2 min read

Bachelor’s degree in quantitative finance, economics, statistics, mathematics, data science, computer science, or a related quantitative discipline.
Master’s degree in financial engineering, data science, applied mathematics, or another quantitative field is preferred.
7+ years of experience in credit risk modeling, model development, model validation, model risk management, forecasting, or related quantitative functions.
Proven experience developing or overseeing Expected Loss models using PD, LGD, and EAD methodologies.
Deep expertise in machine learning, statistical modeling, econometric methods, and data science techniques applied to credit risk and loss forecasting.
Strong experience with credit loss forecasting for CECL, stress testing, CCAR/DFAST, or similar regulatory and risk management applications.
Experience with CECL modeling, implementation, and production of quantitative and qualitative components.
Strong understanding of consumer credit products, including credit cards, personal loans, and unsecured lending.
Familiarity with credit scorecards, origination models, and credit underwriting analytics is highly desirable.
Experience with regulatory frameworks and requirements such as Basel III, IFRS 9, CCAR, Dodd-Frank, and related credit risk standards.
Experience with regulatory stress testing or impairment models is preferred.
Proficiency with analytical and statistical tools such as Python, R, SAS, SQL, or comparable modeling platforms.
Strong understanding of data management, data integrity, and data quality considerations affecting model inputs and outputs.
Strong analytical and problem-solving abilities, with the judgment to identify model weaknesses and determine appropriate adjustments.
Excellent communication and presentation skills, with the ability to translate technical modeling concepts for both technical and non-technical stakeholders.
Strong project leadership skills and the ability to manage priorities, action plans, and deliverables in a regulated environment.
A collaborative, adaptable, and detail-oriented approach, with a commitment to continuous learning and professional development.