Senior Model Risk Manager

Earnies are committed to helping borrowers move forward with confidence by offering smarter borrowing options with a clearer path to taking control of their debt. If you’re as passionate as we are about our mission, read more below, and let’s build something great together. 

• Own and evolve Earnest’s Model Risk Management framework, ensuring our credit, loss forecasting, fraud, marketing, and finance models are rigorous, transparent, and built to scale responsibly.
• Lead independent end-to-end model validations — from conceptual soundness and data quality to performance monitoring and implementation review — delivering thoughtful, constructive challenge to modeling teams.
• Partner closely with Data Science and Risk leaders early in the model design process to strengthen assumptions, improve methodology, and elevate modeling standards across the company.
• Oversee model performance monitoring and proactively identify emerging risks, performance drift, or control gaps — ensuring timely, practical remediation.
• Deliver clear, decision-ready validation reports and communicate technical findings in a way that drives strong business outcomes and sound risk decisions.
• Serve as a trusted advisor on model governance, helping Earnest move fast while maintaining the discipline and controls required of a best-in-class lending platform.

• You hold a Master’s degree in a quantitative field (Statistics, Economics, Mathematics, Financial Engineering, or similar) with 7+ years of experience in model development, validation, or model risk management in financial services (or a Bachelor’s degree with 10+ years of relevant experience).
• You’ve worked in consumer or small business lending — ideally in a fintech or innovative banking environment — and understand the models that power modern credit decisioning.
• You have hands-on experience with statistical and machine learning models and are comfortable diving into Python code and large datasets using SQL to independently assess model performance.
• You understand regulatory model risk expectations and know how to apply them pragmatically in a high-growth environment.
• You bring strong judgment, intellectual independence, and the confidence to challenge assumptions while building productive cross-functional partnerships.
• You can translate complex modeling concepts into clear, actionable insights for senior stakeholders.

• Experience in loss forecasting models, preferably with hands-on exposure to CECL frameworks, including model development, validation, or governance.
• Experience with personal loans or student lending products.
• Experience interacting with auditors or regulators in model risk or credit oversight contexts.
• Experience reviewing third-party or vendor models.
• Experience building or maturing a model governance framework at a scaling fintech company.

• This role will be based in the US.

At Earnest, our people bring our cultural principles to life. These principles define how we work, how we win, and what we expect of ourselves and each other:

• Every Second Counts: Speed is our competitive advantage. Our customers need better solutions, and the faster we execute, the greater our chance of success.
• Choose To Do Hard Things: We win by tackling the hard things that others avoid, fueled by grit and resilience.
• Pursue Excellence: Great companies, teams, and individuals never settle and are proud of the work that they do. What’s good enough today won’t be good enough tomorrow. Excellence isn’t a destination; it’s a mindset of continuous improvement.
• Lead Together: Our success comes from how we work together. Leadership is not about titles—it is about action. We take ownership, drive results, and move forward as a team.
• Don’t Take Yourself Too Seriously: We take our work seriously, not ourselves. The stakes are high, but a sense of humor keeps us grounded, creative, and resilient.

Earnest provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity, veteran status, disability or genetics. Qualified applicants with criminal histories will be considered for the position in a manner consistent with the Fair Chance Ordinance.