Data Scientist, Imprint

• Experience: 4+ years of hands-on experience in data science, analytics, or quantitative credit risk management, ideally within a high-growth fintech, credit card, or consumer lending environment
• Education: Advanced degree (Master’s or PhD) strongly preferred in a quantitative field (e.g., Statistics, Mathematics, Computer Science, Economics, Engineering, Physics). Exceptional candidates with a Bachelor's degree and extensive relevant experience will be considered
• Strong command of Python and relevant data science libraries (pandas, scikit-learn, NumPy, etc.)
• Expertise in SQL for querying and manipulating large datasets
• Deep understanding and practical application of statistical analysis, probability, and machine learning techniques relevant to credit risk (e.g., logistic regression, gradient boosting, tree-based models, clustering, time series analysis)
• Credit Risk Expertise: Solid understanding of the credit lifecycle, consumer lending principles, underwriting analytics, loan economics, portfolio management, and relevant regulatory environments (e.g., fair lending)
• Analytical & Problem-Solving Skills: Proven ability to tackle complex problems, think critically, structure analyses, and drive data-driven solutions in ambiguous environments
• Communication: Excellent ability to translate complex technical concepts and analytical results into clear, actionable insights for diverse audiences (technical and non-technical)
• Collaboration & Ownership: Comfortable owning projects end-to-end, managing multiple priorities, and collaborating effectively in a fast-paced, cross-functional team setting

Desirable

• Join our mission-driven credit card startup as a Data Scientist on the Credit Risk team
• You’ll play a pivotal role in building the analytical and modeling foundation that powers smarter, faster, and safer credit decisions
• Your expertise will guide the development of robust underwriting models, fraud detection systems, and credit policies that balance growth with risk mitigation, enabling us to scale responsibly while delivering innovative credit products
• The Credit Risk & Analytics team is central to Imprint's success, responsible for enabling sustainable growth by managing the inherent risks in lending
• We build and refine the machine learning models and data-driven strategies that underpin our credit decisions, from underwriting new applicants to managing portfolio risk
• We leverage diverse data sources and advanced analytical techniques to optimize credit policies, minimize losses, and ensure regulatory compliance, all while delivering a seamless experience for our partners and customers
• Develop & Enhance Credit Models: Design, develop, implement, and maintain advanced statistical and machine learning models for core credit risk areas (underwriting, fraud detection, credit line assignment, loss forecasting, portfolio management), leveraging traditional credit bureau data and alternative data sources (e.g. , bank transactions, specialty bureaus)
• Data Analysis & Insights: Analyze large, complex datasets to identify trends, patterns, and actionable insights that inform credit policy, risk appetite frameworks, and overall business strategy
• Experimentation & Optimization: Design, execute, and evaluate experiments (including A/B testing and policy change impact analysis) to measure and continuously improve the effectiveness of credit risk strategies and models
• Monitor & Validate Performance: Continuously monitor, validate, and enhance the performance of existing risk models, ensuring robust documentation, adherence to model governance standards, regulatory compliance, and timely updates in response to portfolio performance and market changes
• Cross-Functional Collaboration: Work closely with Product, Engineering, Credit, Operations, Finance, Legal, and Compliance teams to seamlessly integrate data science solutions, deploy models into production, and support end-to-end project delivery
• Innovation & Research: Lead the identification, evaluation, and integration of new data sources and modeling techniques, including emerging ML/AI approaches, to continuously improve risk prediction and decision-making capabilities
• Strategy Support: Support pricing and credit limit strategies through quantitative analysis of risk-adjusted returns and loss forecasting
• Communication: Present complex analytical findings, model results, and strategic recommendations to technical and non-technical stakeholders, including senior management, using clear narratives and effective visualizations