Senior Quantitative Risk Analyst

Earnest•San Francisco, CA

3d•$126,000 - $145,000

About The Position

Our mission is to make higher education accessible and affordable for everyone. We empower students with financial support and supercharge their ability to pay down their debt, so they can get on the right financial track, fast. We build tools that help people feel in control of their financial future, including: Private student loans - low rates, people-first service, and flexible payments. Student loan refinancing - break free from high-interest rates or monthly payments. Scholarships - access to thousands of scholarships to help students pay less. Earnies are committed to helping students live their best lives, free from the stress of student debt. If you’re as passionate as we are about our mission, read more below, and let’s build something great together! The Senior Quantitative Risk Analyst position will report to Senior Risk Analytics Manager.

Requirements

3+ years in a data-driven role, ideally within credit or fraud risk.
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.
Strong analytical thinker with excellent communication and presentation abilities.
Detail-oriented, intellectually curious, and able to manage multiple priorities under tight deadlines.

Nice To Haves

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.
Background in efficiency improvements and automation.
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.