Senior Quantitative Credit Risk Analyst

Wright-Patt Credit Union•Beavercreek, OH

9h•$84,427 - $126,568

About The Position

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.

Requirements

Strong statistical and business judgment
High degree of autonomy
SQL
Python

Nice To Haves

Advanced quantitative analysis
Consumer credit risk management
Underwriting strategy
Portfolio monitoring
Executive decision-making
Credit
Finance
Operations
Compliance
Data teams
Emerging risk trends
Key credit metrics
Strategy and policy changes
Growth, risk, and member outcomes
Accurate, actionable, scalable analysis
Governance and control expectations
Credit Risk Strategy and Executive Decision Support
Analytics partner to Credit and business leadership
Quantitative analysis informing underwriting strategy, portfolio management, line assignment, and other credit decisions
Portfolio performance
Credit strategy
Emerging risk trends
Consumer lending products
Business questions into analytical frameworks
Risk, performance, and expected impact of proposed strategy or policy changes
Risk-reward tradeoffs
Segment performance drivers
Opportunity areas
Sound credit decisions and portfolio actions
Credit Risk Management
Portfolio Management
Risk Appetite / Policy Support
Underwriting and Line Management Insights
Loss Forecasting / Reserve Support
Vintage, Segmentation, and Stress Analysis
Regulatory / Governance Discipline
Decision Science tied to Credit Outcomes
Decision-ready insights
Portfolio performance, key risks, root causes, and recommended actions for leadership
Portfolio Monitoring, Risk Measurement, and Governance
Credit risk measurement frameworks
Ongoing monitoring, consistent reporting, and accountability for portfolio performance
Key credit metrics, portfolio segmentation approaches, and monitoring standards
Delinquency, losses, recoveries, utilization, exposure, and related performance indicators
Baselines, thresholds, and reporting routines
Performance against forecast, plan, and risk tolerance
Reporting highlighting vintage trends, segment migration, concentration risk, and early warning indicators
Risk reporting integrity
Validating assumptions
Improving data consistency
Aligning analysis with policy, governance, and control requirements
Advanced Quantitative Analysis, Forecasting, and Statistical Rigor
Disciplined quantitative methods
Performance drivers, evaluating changes, and forecasting credit outcomes
Vintage, cohort, segmentation, roll-rate, and migration analysis
Changes in portfolio quality and performance
Statistical methods such as regression, hypothesis testing, sensitivity analysis, and forecasting
Interpreting outcomes and supporting credit strategy decisions
Impact of underwriting, pricing, line management, or collections strategy changes
Structured analytical approaches and repeatable standards
Confidence levels, limitations, and practical significance
Sound business judgment and governance decisions
Executive Reporting and Cross-Functional Influence
Concise, high-quality reports, presentations, and briefing materials
Translating complex credit performance data into clear actions for senior leadership and risk stakeholders
Portfolio insights, emerging risks, and strategy recommendations
Concise, business-focused format
Clear summaries, dashboards, and recommendations
Connecting analytical results to decisions and risk outcomes
Assumptions, tradeoffs, and limitations
Implications of decisions and changing conditions
Prioritization and action
Stakeholder partnership, clear communication, and credible analytical support
Cross-Functional Collaboration, Data Enablement, and Control Support
Credit, Finance, Operations, Compliance, Technology, and data teams
Analytical efficiency, strengthening risk reporting, and supporting governed use of data and models
Reusable workflows and automation
Analysis speed, repeatability, and control
Data quality, dataset usability, and access to credit-relevant information
Monitoring and alerting practices
Meaningful changes in portfolio risk and performance
Model outputs, performance trends, and analytical findings
Practical recommendations for business partners
Policies, procedures, risk mitigation activities, and operating controls
Escalating gaps or concerns to leadership
Risk appropriately managed

Responsibilities

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.
Deliver decision-ready insights that explain portfolio performance, key risks, root causes, and recommended actions for leadership.
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.
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.
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.
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 escalate gaps or concerns to leadership so risk is appropriately managed.