Earnest Staff Data Scientist leading risk data science initiatives including credit risk, underwriting, fraud detection, and loss forecasting. Mentors team members and partners cross-functionally to align risk strategies with company goals.
Responsibilities
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.
Qualification
Master's/PhD in EconomicsPrior experience in consumer lending
Required
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.
