Staff Credit Risk Analyst

The base salary range for this full-time position is $173,928 – $245,773 plus equity and benefits. Our salary ranges are determined by role, level, and location. This is a remote role in the US, however those local to Mountain View (headquarters) will require in-office work 2 days a week.

 

• 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

WHAT WE'RE LOOKING FOIR 

• 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

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