Staff Risk Analyst

EarnIn

1d•$173,928 - $245,773•Hybrid

About The Position

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. 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.

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