Staff Data Scientist
New
E
EarnestConsumer lending
This role will be based in the US.Full-TimeStaff
Salary$216k - $262k USD. Employees are also eligible for an annual performance-based bonus and equity.
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Job Details
- Experience
- 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
- Required Skills
- PythonSQLMachine LearningR
Requirements
- Have 8+ years of experience in Python/R and SQL.
- Have 7+ years of professional experience in quantitative risk modeling, credit risk analytics, fraud analytics, or applied data science.
- Hold a master's or PhD in Economics, Statistics, Computer Science, Operations Research, or a related quantitative field.
- Have consumer lending, fintech, or financial services experience with direct technical ownership of credit underwriting or fraud models.
- Demonstrate expertise in credit risk modeling, default prediction (PD, LGD, EAD), survival analysis, machine learning algorithms, and risk-adjusted return frameworks.
- Have deep proficiency in model lifecycle management, model validation, backtesting, and monitoring in credit or fraud risk environments.
- Have a track record of independently owning and delivering complex cross-functional modeling initiatives end-to-end.
- Have experience mentoring or managing junior data scientists and contributing to technical and governance standards.
- Communicate technical concepts clearly and influence stakeholders and senior audiences.
- Preferred: understand financial metrics and consumer credit regulations such as FCRA and ECOA/Fair Lending.
- Preferred: have experience with AWS, GCP, or Azure, and tools such as Looker, Airflow, and dbt.
- Preferred: have experience with non-traditional data sources or LLM/NLP tools applied to underwriting, fraud detection, or document processing.
Responsibilities
- Own the end-to-end development and delivery of risk science systems for credit risk scoring, default prediction, fraud detection, and collection scores.
- Design, backtest, and validate credit risk models and loss forecasting frameworks using statistical techniques and machine learning.
- Serve as a subject matter expert on credit risk modeling, loss dynamics, and risk-based decisioning engines.
- Lead cross-functional risk modeling projects from conception through production deployment.
- Present risk models, loss projections, and analytical findings to senior leadership, the Risk Committee, and non-technical stakeholders.
- Mentor data scientists and help establish modeling standards, governance, and analytical rigor.
- Partner with Risk, Pricing, Product, Capital Markets, Engineering, and Compliance leadership on risk strategies.
- Oversee production risk and fraud pipelines, including automated monitoring, stress testing, and alerting.
- Partner with the MRM team on model validation, fair lending analysis, model governance, and technical documentation.
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