VP Data Scientist

The Data Scientist, will play a critical role in the development of data-driven solutions that enhance credit risk assessment, underwriting, pricing, and portfolio performance within our commercial vertical. From developing credit scoring models to identifying industry based signals and optimizing underwriting would be areas of impact for this role. The role will collaborate cross-functionally with stakeholders across Credit Risk, Product, Engineering, and Compliance to deploy models that are both technically robust and aligned with regulatory standards Requirements Design, build, and maintain predictive models related to credit risk, loan performance, and customer segmentation Analyze large and complex datasets to extract insights that inform credit strategy and operational decision-making. Analyze merchant behavior and historic deal performance to inform risk, pricing, and retention strategies Work closely with Credit Risk and Underwriting teams to refine credit policies and evaluate merchant eligibility Collaborate with engineering, product, and underwriting teams to integrate models into production systems Clean, structure, and analyze large structured and unstructured datasets (e.g., financials, cash flow data, application data, behavioral data). Conduct A/B tests and experiments to evaluate product and policy changes. Communicate findings and recommendations to both technical and non-technical stakeholders through clear documentation, visualizations, and presentations. Develop and maintain monitoring tools to track model performance and ensure compliance with internal governance and regulatory frameworks Required Experience & Skills: 7+ years of professional experience in data science, preferably within a fintech, financial services, or lending environment. Bachelor’s or Master’s degree in a quantitative field such as Statistics, Computer Science, Economics, Applied Mathematics, or related discipline. Proficiency in SQL, and Python/R. Experience with statistical modeling, machine learning, and predictive analytics. Solid understanding of credit risk modeling or financial services analytics. Ability to translate complex data into business recommendations Qualifications: Experience in small business lending, fintech, or alternative credit data. Familiarity with model governance and explainability techniques (e.g., SHAP) Exposure to cloud platforms (e.g., AWS) and data engineering workflows Advanced degree (Master’s or PhD) in a quantitative field such as Statistics, Computer Science, Economics, or similar would be a plus Understanding of model governance, regulatory requirements, and compliance standards