Earnest aims to make higher education accessible and affordable by empowering students with financial support and helping them manage their student debt. The Senior Quantitative Risk Analyst will contribute to this mission by forecasting losses and analyzing credit metrics for their loan products.
Requirements
- Strong proficiency in SQL for data extraction and Python for statistical modeling and analysis.
- Experience with loss forecasting, particularly in consumer lending products.
- Skilled in data visualization and BI tools (e.g., Looker, Tableau) as well as Excel and PowerPoint.
- Experience in student loans, personal loans, or credit cards.
- Familiarity with regulatory compliance in the lending industry.
- Prior exposure to risk model development or enhancement.
- Ability to independently research and implement innovative modeling approaches.
Responsibilities
- Lead loss forecasting by leveraging data science models and applying business overlays to project portfolio performance.
- Monitor and analyze credit metrics across multiple loan products, identifying key risks, trends, and opportunities.
- Prepare and present portfolio forecasts and credit/fraud performance updates to executives, the Board, and rating agencies.
- Validate and challenge credit and fraud risk models to ensure methodological soundness, transparency, and regulatory compliance.
- Partner cross-functionally with Finance, Capital Markets, and Data Science to align risk insights with business strategy.
Other
- 3+ years in a data-driven role, ideally within credit or fraud risk.
- Strong analytical thinker with excellent communication and presentation abilities.
- Detail-oriented, intellectually curious, and able to manage multiple priorities under tight deadlines.
- Background in efficiency improvements and automation.
- This role will be based in the US.
