Staff Data Scientist

Earnest•San Francisco, CA

About The Position

The Staff Data Scientist will own risk data science at Earnest, developing credit risk, underwriting, fraud detection, collections, and loss forecasting frameworks that shape portfolio health and the unit economics of lending products. The role leads high-impact modeling initiatives, mentors data scientists, and partners with senior stakeholders across Risk, Product, Capital Markets, Engineering, Finance, and Compliance. The position reports to the Sr. Manager, Data Science + Pricing and is based in the US. The role encompasses end-to-end development and delivery of risk science systems, including credit risk scoring, default prediction (PD/LGD), fraud detection, and collection scores. It also involves designing, backtesting, and validating credit risk models and loss forecasting frameworks; serving as a subject matter expert on credit risk modeling and risk-based decisioning; and independently leading cross-functional projects from conception through production deployment. The Staff Data Scientist communicates models and analytical findings to senior leadership and non-technical stakeholders; mentors data scientists and establishes modeling and governance standards; aligns risk strategies with company growth, funding needs, and regulatory obligations; and works with data and ML engineering teams to deploy production-grade risk and fraud pipelines with monitoring, stress testing, and alerting. The role also partners with the MRM team on model validation, fair lending analysis, governance, and technical documentation.

Requirements

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 or 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 such as Gradient Boosted Trees and Logistic Regression, and risk-adjusted return frameworks.
Deep proficiency in model lifecycle management, model validation, backtesting, and monitoring in credit or fraud risk environments.
Track record of independently owning and delivering complex, cross-functional modeling initiatives end-to-end.
Experience mentoring or managing junior data scientists and contributing to team-wide technical and governance standards.
Strong communication skills, including the ability to influence stakeholders and present technical concepts clearly to senior audiences.

Nice To Haves

Solid understanding of financial metrics and consumer credit regulations such as FCRA and ECOA/Fair Lending.
Experience with cloud production platforms such as AWS, GCP, or Azure.
Proficiency with modern data engineering and analytics tooling such as Looker, Airflow, and dbt.
Experience using non-traditional data sources or LLM/NLP tools for underwriting, fraud detection, or document processing.

Responsibilities

Own end-to-end development and delivery of risk science systems, including credit risk scoring, default prediction (PD/LGD), fraud detection, and collection scores.
Lead the design, backtesting, and validation of credit risk models and loss forecasting frameworks using statistical techniques and machine learning.
Serve as the internal subject matter expert on credit risk modeling, loss dynamics, and risk-based decisioning engines.
Lead high-visibility, cross-functional risk modeling projects from conception through production deployment, managing timelines and stakeholder expectations.
Translate risk models, loss projections, and analytical findings into clear narratives for senior leadership, the Risk Committee, and non-technical stakeholders.
Mentor and provide technical guidance to data scientists, and help establish modeling standards, governance, and analytical rigor.
Partner with Risk, Pricing, Product, Capital Markets, Engineering, and Compliance leadership to align risk strategies with company growth, funding requirements, and regulatory obligations.
Oversee deployment of scalable, production-grade risk and fraud pipelines with data and ML engineering teams, including monitoring, stress testing, and alerting.
Partner with the MRM team on model validation, fair lending analysis, model governance, and technical documentation.