E
EarnIn
Staff Credit Risk Analyst
Skills
SQL QueriesPythonData ScienceData AnalysisMachine LearningRisk Management
21 days ago
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Important information
Salary:
$173,928 – $245,773 / year
Location:
United States
Work mode:
Remote
Published on:
12 October 2026
What they need
Who you are
- 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
What the job involves
- Our Risk team takes a unique approach to risk management, portfolio management, and interacting with our community members
- As a Staff Risk Analyst, you'll turn information into insights through analytics, data science, and experimentation to help the company achieve tremendous growth
- You'll represent risk analytics for credit risk management, with a focus on limit/max policy, member retention, and forecasting
- 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
Benefits
- 401k
- FSA
- Mental health resources
- Employee Resource Groups
- Stipend for growth opportunities
- Pet friendly office
- Work from home opportunities
- Health insurance
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