Job Description
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. 1) 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. a) Lead complex analyses tied to portfolio performance, credit strategy, and emerging risk trends across consumer lending products. b) Translate business questions into analytical frameworks that evaluate risk, performance, and the expected impact of proposed strategy or policy changes. c) Quantify risk-reward tradeoffs, segment performance drivers, and opportunity areas to support sound credit decisions and portfolio actions. i. Credit Risk Management ii. Portfolio Management iii. Risk Appetite / Policy Support iv. Underwriting and Line Management Insights v. Loss Forecasting / Reserve Support vi. Vintage, Segmentation, and Stress Analysis vii.
Regulatory / Governance
Discipline viii. Decision Science tied to Credit Outcomes d) Deliver decision-ready insights that explain portfolio performance, key risks, root causes, and recommended actions for leadership. 2) 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. a) Define key credit metrics, portfolio segmentation approaches, and monitoring standards for delinquency, losses, recoveries, utilization, exposure, and related performance indicators. b) Establish baselines, thresholds, and reporting routines that allow leaders to track performance against forecast, plan, and risk tolerance. c) Build and enhance reporting that highlights vintage trends, segment migration, concentration risk, and early warning indicators across the portfolio. d) Ensure risk reporting integrity by validating assumptions, improving data consistency, and aligning analysis with policy, governance, and control requirements. 3) 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. a) Lead vintage, cohort, segmentation, roll-rate, and migration analysis to identify changes in portfolio quality and performance. b) Apply statistical methods such as regression, hypothesis testing, sensitivity analysis, and forecasting to interpret outcomes and support credit strategy decisions. c) Evaluate the impact of underwriting, pricing, line management, or collections strategy changes using structured analytical approaches and repeatable standards. d) Communicate confidence levels, limitations, and practical significance in a way that supports sound business judgment and governance decisions. 4) 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. a) Present portfolio insights, emerging risks, and strategy recommendations to senior leaders in a concise, business-focused format. b) Create clear summaries, dashboards, and recommendations that connect analytical results to decisions and risk outcomes. c) Communicate assumptions, tradeoffs, and limitations clearly so leaders understand the implications of decisions and changing conditions. d) Influence prioritization and action through strong stakeholder partnership, clear communication, and credible analytical support. 5) 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. a) Develop reusable workflows and automation using SQL and Python to improve analysis speed, repeatability, and control. b) Partner with data and technology teams to improve data quality, dataset usability, and access to credit-relevant information. c) Support monitoring and alerting practices that surface meaningful changes in portfolio risk and performance in a timely manner. d) Interpret model outputs, performance trends, and analytical findings and translate them into practical recommendations for business partners. e) Ensure policies, procedures, risk mitigation activities, and operating controls are followed, and escalate gaps or concerns to leadership so risk is appropriately managed.
