Senior/Staff Credit Data Scientist

Splash Financial•Only, TN

2d•$143,000 - $220,000•Remote

About The Position

Splash Financial is seeking a Senior/Staff Credit Data Scientist with deep expertise in consumer lending and credit risk analytics. The role uses traditional credit bureau data and alternative data sources to develop predictive models, optimize credit and pricing strategies, and support responsible, scalable lending growth. The successful candidate will apply rigorous statistical methods, uphold high standards for data quality and model performance, and translate analytical findings into actionable business recommendations. The position involves ownership of high-impact initiatives, collaboration across Credit Risk, Product, Engineering, and Finance, and mentoring colleagues. The role also contributes to the future of credit decisioning and lending at Splash Financial. Splash is remote-first, with a fast-paced, startup-style workplace. The role is described as fully remote for most positions.

Requirements

Bachelor's degree or foreign equivalent in Statistics, Mathematics, Economics, Computer Science, Data Science, Engineering, or a closely related quantitative field providing specialized knowledge applicable to credit risk and pricing analytics.
At least 8 years of progressively responsible professional experience in credit risk analytics, pricing analytics, statistical modeling, or related quantitative functions within consumer lending; unsecured consumer lending or fintech experience is preferred.
Advanced proficiency in Python for statistical analysis, predictive modeling, data manipulation, and development of production-quality analytical solutions.
Demonstrated expertise in statistical and quantitative modeling, including regression analysis, predictive modeling, feature engineering, model validation, and performance evaluation.
Strong understanding of consumer lending credit risk and pricing methodologies, including credit decisioning, risk assessment, loan performance analysis, and risk-based pricing optimization.
Ability to apply advanced quantitative methodologies to complex financial datasets, interpret findings, and develop data-driven recommendations for credit and pricing strategies.
Strong communication and collaboration skills, including the ability to explain complex analytical concepts as actionable business insights, give and receive constructive feedback, and contribute to cross-functional decisions.

Nice To Haves

Master's degree or PhD in a related discipline.
Experience in unsecured consumer lending or a fintech environment.

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 that optimize risk-adjusted returns, loan performance, and conversion.
Develop and evaluate AI-powered agents and workflows to enhance underwriting, pricing, and analytical operations.
Apply advanced 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 changing portfolio trends and realized outcomes.
Document modeling methodologies and incorporate applicable model governance, fair lending, data privacy, and responsible AI considerations.
Lead complex data science initiatives, mentor colleagues, and provide constructive feedback on analytical approaches and technical implementations.
Collaborate cross-functionally with Credit Risk, Product, Engineering, and Finance to translate business challenges into actionable insights and measurable outcomes.