Senior/Staff Credit Data Scientist
New
S
Splash FinancialConsumer lending
Remote - United States OnlyFull-TimeStaff
SalaryThe base salary range for this role is $143,000 to $220,000 annually. This role may also be eligible for a bonus component tied to individual and company performance.
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Job Details
- Experience
- Minimum of 8 years of progressively responsible professional experience in credit risk analytics, pricing analytics, statistical modeling, or related quantitative functions within consumer lending.
- Required Skills
- Python
Requirements
- Hold a bachelor's degree or foreign equivalent in Statistics, Mathematics, Economics, Computer Science, Data Science, Engineering, or a closely related quantitative field.
- A master's degree or PhD in a related discipline is strongly preferred.
- Have at least 8 years of progressively responsible experience in credit risk analytics, pricing analytics, statistical modeling, or related quantitative work within consumer lending.
- Bring experience in unsecured consumer lending or a fintech environment, preferably.
- Demonstrate advanced proficiency in Python for statistical analysis, predictive modeling, data manipulation, and production-quality analytical solutions.
- Demonstrate expertise in regression analysis, predictive modeling, feature engineering, model validation, and performance evaluation.
- Show a strong understanding of consumer lending credit risk and pricing methodologies, including credit decisioning, risk assessment, loan performance analysis, and risk-based pricing optimization.
- Apply advanced quantitative methods to complex financial datasets and develop data-driven recommendations for credit and pricing strategies.
- Translate complex analytical concepts into actionable business insights and contribute to cross-functional decision-making.
Responsibilities
- Develop and deploy statistical and machine learning models to predict credit risk, delinquency, defaults, and loan losses.
- Analyze credit bureau, alternative, and loan performance data to improve underwriting, risk segmentation, and credit decisioning.
- Build quantitative pricing models to optimize risk-adjusted returns, loan performance, and conversion.
- Develop and evaluate AI-powered agents and workflows for underwriting, pricing, and analytical operations.
- Apply statistical methods, feature engineering, and model validation techniques to improve predictive accuracy and reliability.
- Partner with Engineering to deploy scalable, production-ready models and analytical pipelines using Python and other data science tools.
- Monitor model performance, investigate data anomalies, and implement improvements based on portfolio trends and realized outcomes.
- Document modeling methodologies and incorporate model governance, fair lending, data privacy, and responsible AI considerations.
- Lead complex data science initiatives, mentor colleagues, and provide feedback on analytical approaches and technical implementations.
- Collaborate with Credit Risk, Product, Engineering, and Finance to translate business challenges into actionable insights and measurable outcomes.
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