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Jobs(26,636)/Credit Analyst Role(19)/EarnIn (1) - Staff Credit Risk Analyst
EarnIn

EarnIn - Staff Credit Risk Analyst

Remote, US$174k - $246k+ Equity3w ago
In OfficeStaffNAFintechArtificial IntelligenceCredit AnalystFinancial Quantitative AnalystLearning & DevelopmentSQLPythonReportingRisk Management

Requirements

• 7+ years of experience in a credit risk analytics, decision science, or risk strategy role, ideally within fintech or consumer financial products • Expert in SQL; comfortable with Python for analysis and experimentation • Working knowledge of fintech risk fundamentals, including risk strategy development, limit/max policy, and portfolio monitoring • Experience designing and evaluating A/B tests or policy experiments, and translating results into recommendations • Comfortable working with forecasting or curve-based modeling (e.g., cash flow, loss, or usage forecasts) • Strong communicator who can translate analytical findings into clear, actionable business recommendations • Able to work cross-functionally with Machine Learning, Product, Engineering, and Operations • Ability to think creatively and thrive in a fast-paced, dynamic, and often ambiguous environment

Responsibilities

• Own policy optimization for member credit limits, balancing retention, churn, and risk exposure • Design, run, and evaluate policy experiments and A/B tests, translating results into concrete policy changes • Own forecasting and reporting infrastructure that keeps risk and portfolio performance visible to stakeholders • Proactively explore data to identify opportunities to refine risk management strategies and surface emerging risk trends • Define performance metrics and build reports/dashboards to monitor policy and portfolio performance • Partner closely with Machine Learning, Product, Engineering, and Operations to translate analysis into shipped policy and product changes • Work closely with the Machine Learning team throughout the model development lifecycle, helping shape how risk models are built since you'll be a primary consumer of their outputs • Monitor deployed models in production, tracking performance and stability over time and flagging drift or degradation that should inform retraining or redesign • Partner with Product to understand customer anecdotes and pain points, and design risk policy with customer experience in mind alongside risk and business outcomes

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