Senior Quantitative Credit Risk Analyst

Wright-Patt Credit Union; Beavercreek (OH); On-site; USD 100,000 - 115,000; Full time

Job summary

Wright-Patt Credit Union in Beavercreek, OH seeks a Senior Quantitative Credit Risk Analyst to lead advanced analytics for consumer lending, underwriting strategy, and portfolio monitoring. You will partner with Credit, Finance, Operations and data teams to identify risk trends and deliver actionable recommendations that balance growth, risk, and member outcomes.

This role emphasizes autonomy, uses SQL and Python for modelling, forecasting, and reporting, and requires strong statistical skills,

Qualifications

• Bachelor’s degree in Finance, Economics, Statistics, Mathematics, Analytics, CS, Engineering, or related quantitative field.
• 7+ years of experience in credit risk analytics, portfolio risk management, underwriting analytics, or related roles in financial services.
• Advanced proficiency in SQL for complex analysis, data validation, portfolio monitoring, and dataset development.
• Strong proficiency in Python for analytics, forecasting, and automation, including development of reusable workflows.
• Strong foundation in statistical methods including regression, hypothesis testing, sensitivity analysis, forecasting, and segmentation analysis.

Responsibilities

• Credit Risk Strategy and Executive Decision Support: Deliver quantitative analysis informing underwriting strategy and portfolio management.
• Lead complex analyses tied to portfolio performance, credit strategy, and emerging risk trends.
• Translate business questions into analytical frameworks evaluating risk and impact of changes.
• Quantify risk-reward tradeoffs and segment performance drivers to support decisions.
• Portfolio Monitoring, Risk Measurement, and Governance: Define metrics, segmentation, and monitoring standards; build reporting.
• Advanced Quantitative Analysis, Forecasting, and Statistical Rigor: Apply disciplined methods to understand drivers and forecast outcomes.
• Executive Reporting and Cross-Functional Influence: Prepare concise reports and briefings for senior leadership.
• Cross-Functional Collaboration, Data Enablement, and Control Support: Improve workflows; ensure data quality and governance.

Skills

SQL

Python

Statistics

Credit risk

Portfolio monitoring

Executive reporting

Leadership

Data governance

Education

Bachelor’s degree or higher in a quantitative field

Tools

Power BI

Job description

Credit Risk Management

Department

Consumer Lending

Job Location

3560 Pentagon Blvd, Beavercreek, Ohio, United States

Position Type

Full-Time/Regular

Work Type

Variable

NMLS Required

No

Pay Classification

Exempt (Salary)

Grade and Compensation Band

PG 18: $84,427.20 – $126,568.00 (Annually)

Target Compensation

$100,000.00 to $115,000.0

Role Overview

The Senior Quantitative Credit Risk Analyst leads advanced quantitative analysis that supports consumer credit risk management, underwriting strategy, portfolio monitoring, and executive decision-making. This role partners closely with Credit, Finance, Operations, Compliance, and data teams to identify emerging risk trends, define and monitor key credit metrics, evaluate strategy and policy changes, and deliver clear recommendations that balance growth, risk, and member outcomes. The Senior Quantitative Credit Risk Analyst operates with a high degree of autonomy, applies strong statistical and business judgment, and helps ensure that credit risk analysis is accurate, actionable, scalable, and aligned with governance and control expectations.

Responsibilities

• Credit Risk Strategy and Executive Decision Support (30%): Serve as a primary analytics partner to Credit and business leadership by delivering quantitative analysis that informs underwriting strategy, portfolio management, line assignment, and other credit decisions.
• Lead complex analyses tied to portfolio performance, credit strategy, and emerging risk trends across consumer lending products.
• Translate business questions into analytical frameworks that evaluate risk, performance, and the expected impact of proposed strategy or policy changes.
• Quantify risk-reward tradeoffs, segment performance drivers, and opportunity areas to support sound credit decisions and portfolio actions.
• Credit Risk Management
• ii. Portfolio Management
• iii. Risk Appetite / Policy Support
• iv. Underwriting and Line Management Insights
• vi. Loss Forecasting / Reserve Support
• vi. Vintage, Segmentation, and Stress Analysis
• vii. Regulatory / Governance Discipline
• viii. Decision Science tied to Credit Outcomes
• Deliver decision-ready insights that explain portfolio performance, key risks, root causes, and recommended actions for leadership.
• Portfolio Monitoring, Risk Measurement, and Governance (25%): Design and maintain credit risk measurement frameworks that support ongoing monitoring, consistent reporting, and accountability for portfolio performance.
• Define key credit metrics, portfolio segmentation approaches, and monitoring standards for delinquency, losses, recoveries, utilization, exposure, and related performance indicators.
• Establish baselines, thresholds, and reporting routines that allow leaders to track performance against forecast, plan, and risk tolerance.
• Build and enhance reporting that highlights vintage trends, segment migration, concentration risk, and early warning indicators across the portfolio.
• Ensure risk reporting integrity by validating assumptions, improving data consistency, and aligning analysis with policy, governance, and control requirements.
• Advanced Quantitative Analysis, Forecasting, and Statistical Rigor (20%): Strengthen decision-making by applying disciplined quantitative methods to understand performance drivers, evaluate changes, and forecast credit outcomes.
• Lead vintage, cohort, segmentation, roll-rate, and migration analysis to identify changes in portfolio quality and performance.
• Apply statistical methods such as regression, hypothesis testing, sensitivity analysis, and forecasting to interpret outcomes and support credit strategy decisions.
• Evaluate the impact of underwriting, pricing, line management, or collections strategy changes using structured analytical approaches and repeatable standards.
• Communicate confidence levels, limitations, and practical significance in a way that supports sound business judgment and governance decisions.
• Executive Reporting and Cross-Functional Influence (15%): Prepare concise, high-quality reports, presentations, and briefing materials that translate complex credit performance data into clear actions for senior leadership and risk stakeholders.
• Present portfolio insights, emerging risks, and strategy recommendations to senior leaders in a concise, business-focused format.
• Create clear summaries, dashboards, and recommendations that connect analytical results to decisions and risk outcomes.
• Communicate assumptions, tradeoffs, and limitations clearly so leaders understand the implications of decisions and changing conditions.
• Influence prioritization and action through strong stakeholder partnership, clear communication, and credible analytical support.
• Cross-Functional Collaboration, Data Enablement, and Control Support (10%): Partner with Credit, Finance, Operations, Compliance, Technology, and data teams to improve analytical efficiency, strengthen risk reporting, and support governed use of data and models.
• Develop reusable workflows and automation using SQL and Python to improve analysis speed, repeatability, and control.
• Partner with data and technology teams to improve data quality, dataset usability, and access to credit-relevant information.
• Support monitoring and alerting practices that surface meaningful changes in portfolio risk and performance in a timely manner.
• Interpret model outputs, performance trends, and analytical findings and translate them into practical recommendations for business partners.
• Ensure policies, procedures, risk mitigation activities, and operating controls are followed, and elevate gaps or concerns to leadership so risk is appropriately managed.

Required Skills

• Bachelor’s degree in Finance, Economics, Statistics, Mathematics, Analytics, Computer Science, Engineering, or a related quantitative field. Master’s degree preferred.
• 7+ years of experience in credit risk analytics, portfolio risk management, underwriting analytics, quantitative finance, or related roles in financial services.
• Advanced proficiency in SQL for complex analysis, data validation, portfolio monitoring, and dataset development.
• Strong proficiency in Python for analytics, forecasting, and automation, including development of reusable workflows.
• Strong foundation in statistical methods including regression, hypothesis testing, sensitivity analysis, forecasting, and segmentation analysis.
• Strong understanding of credit risk metrics, portfolio monitoring, vintage analysis, loss trends, and performance reporting.
• Strong visualization and executive reporting skills in Power BI or similar business intelligence tools.
• Proven ability to lead complex, cross-functional analytical work with minimal oversight and strong attention to governance and control expectations.
• Experience partnering with Credit, Finance, Operations, Compliance, and business leaders to influence credit strategy and risk decisions.
• Strong executive presence and ability to present complex credit risk findings to senior leaders with clarity and confidence.
• Demonstrated ability to balance analytical rigor with practical business judgment, speed, and decision usefulness.
• Experience with underwriting strategy, line management, loss forecasting, model monitoring, or collections analytics preferred.