ABOUT THE ROLE:

Splash Financial is seeking a Senior/Staff Credit Data Scientist with deep expertise in consumer lending and credit risk analytics. In this role, you will leverage traditional credit bureau data and alternative data sources to develop predictive models, optimize credit and pricing strategies, and drive data-informed decisions that support responsible and scalable lending growth.

The ideal candidate is a proactive, analytical problem-solver who takes ownership of their work and thrives in a collaborative, fast-paced environment. You will apply rigorous statistical methodologies, maintain high standards for data quality and model performance, and approach complex challenges with curiosity and a solutions-oriented mindset. Success in this role requires strong technical expertise, sound judgment, and the ability to translate sophisticated analytical findings into actionable business recommendations.

At Splash, we value accountability, continuous improvement, and open communication. We encourage thoughtful debate, constructive feedback, and a willingness to challenge assumptions while remaining aligned on shared goals. Our team is built on mutual respect, collaboration, and a commitment to delivering meaningful results.

Join a team that values innovation, professional growth, flexibility, and work-life balance. You'll have the opportunity to take ownership of high-impact initiatives, collaborate with talented colleagues, mentor others, and help shape the future of credit decisioning and lending at Splash Financial.

WHAT YOU’LL DO AT SPLASH:

• 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.

WHAT YOU’LL BRING TO SPLASH:

• Bachelor's degree or foreign equivalent in Statistics, Mathematics, Economics, Computer Science, Data Science, Engineering, or a closely related quantitative field that provides specialized knowledge directly applicable to credit risk and pricing analytics. A Master's degree or PhD in a related discipline is strongly preferred.
• Minimum of 8 years of progressively responsible professional experience in credit risk analytics, pricing analytics, statistical modeling, or related quantitative functions within consumer lending, preferably in unsecured consumer lending or a fintech environment.
• Advanced proficiency in Python, including its application to statistical analysis, predictive modeling, data manipulation, and the development of production-quality analytical solutions.
• Demonstrated expertise in statistical and quantitative modeling techniques, 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.
• Proven ability to apply advanced quantitative methodologies to complex financial datasets, interpret analytical findings, and develop data-driven recommendations that inform credit and pricing strategies.
• Strong communication and collaboration skills, with the ability to translate complex analytical concepts into actionable business insights, provide and receive constructive feedback, and contribute effectively to cross-functional decision-making.

COMPENSATION:

The base salary range for this role is $143,000 to $220,000 annually, based on market data and internal compensation practices. This role may also be eligible for a bonus component tied to individual and company performance. Final compensation will be determined by factors such as location, experience, and skill level.