Staff Data Scientist

Earnest empowers ambitious professionals to make confident financial decisions and build the life they envision. 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. The Staff Data Scientist will report to Sr. Manager, Data Science + Pricing. You will be the primary owner of risk data science at Earnest, driving the development of sophisticated credit risk, underwriting, fraud detection, collections, and loss forecasting frameworks that directly shape portfolio health and the unit economics of our lending products. You will lead high-impact modeling initiatives, mentor junior team members, and serve as a key cross-functional partner to senior stakeholders across Risk, Product, Capital Markets, Engineering, and Finance. As the Staff Data Scientist, you will: - Own the end-to-end development and delivery of Risk Science systems — including credit risk scoring, default prediction (PD/LGD), fraud detection, and collection scores — that balance loan volume growth with risk-adjusted profitability. - Lead the design, backtesting, and validation of credit risk models and loss forecasting frameworks, applying advanced statistical techniques and machine learning to evaluate portfolio performance. - Serve as the internal subject matter expert on credit risk modeling, loss dynamics, and risk-based decisioning engines, shaping how the organization approaches credit policy and risk management. - Independently lead high-visibility, cross-functional risk modeling projects from conception through production deployment, proactively managing timelines and stakeholder expectations. - Translate complex risk models, loss projections, and analytical findings into clear, compelling narratives for senior leadership, the Risk Committee, and non-technical stakeholders. - Mentor and provide technical guidance to junior and senior data scientists, helping establish modeling standards, robust governance, and a culture of analytical rigor.

- Partner closely with Risk, Pricing, Product, Capital Markets, Engineering, and Compliance leadership to align risk strategies with company-wide growth, funding requirements, and regulatory obligations. - Oversee the deployment of scalable, production-grade risk and fraud pipelines in collaboration with data and ML engineering teams, including automated monitoring, stress testing, and alerting. - Partner closely with the MRM team for best practices in model validation, fair lending analysis, model governance, and comprehensive technical documentation across the data science team. About You: - 8+ years of experience in Python/R and SQL. - 7+ years of professional experience in quantitative risk modeling, credit risk analytics, fraud analytics, or applied data science - Master's/PhD in Economics, Statistics, Computer Science, Operations Research, or a related quantitative field - Prior experience in consumer lending, fintech, or financial services with direct technical ownership of credit underwriting or fraud models. - Strong expertise in credit risk modeling, default prediction (PD, LGD, EAD), survival analysis, machine learning algorithms (e. g. , Gradient Boosted Trees, Logistic Regression), and risk-adjusted return frameworks. - Deep proficiency in model lifecycle management, model validation, backtesting, and monitoring applied to credit or fraud risk environments. - Proven track record of independently owning and delivering complex, cross-functional modeling initiatives end-to-end. - Experience mentoring/managing junior data scientists and contributing to team-wide technical and governance standards. - Strong communication skills with the ability to influence stakeholders and present technical concepts clearly to senior audiences.