Data Scientist, Credit Risk

Who We Are

Imprint is reimagining co-branded credit cards & financial products to be smarter, more rewarding, and truly brand-first. We partner with companies like H-E-B, Turkish Airlines, Brooks Brothers, and Eddie Bauer to launch modern credit programs that deepen loyalty, unlock savings, and drive growth. Our platform combines advanced payments infrastructure, intelligent underwriting, and seamless UX to help brands offer powerful financial products—without becoming a bank.

Co-branded cards account for over $300 billion in U.S. annual spend—but most are still powered by legacy banks. Imprint is the modern alternative: flexible, tech-forward, and built for today’s consumer. Backed by Kleiner Perkins, Thrive Capital, and Khosla Ventures, we’re building a world-class team to redefine how people pay—and how brands grow. If you want to work fast, solve hard problems, and make a real impact, we’d love to meet you.

The Role

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 Team

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.

What You’ll Do

• 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.

What We Look For

• Experience: 6+ 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.

• Technical Proficiency:

• 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.

Preferred Qualifications / Bonus Points For

• Experience mentoring junior data scientists or leading project teams.

• Familiarity with credit bureau data attributes, alternative data sources (e.g., cash flow, transactional), and credit scoring methodologies.

• Deeper knowledge of compliance requirements and model governance/validation processes related to credit risk and lending.

• Experience building or scaling experimentation infrastructure or model monitoring systems.

• Experience with data visualization tools (e.g., Looker, Tableau, Sigma).

• Experience working with cloud platforms (e.g., AWS, GCP, Azure) and associated ML tools (e.g., SageMaker, Vertex AI).

• Exposure to credit card lifecycle management, collections strategy, or fraud risk modeling.

Imprint is committed to a diverse and inclusive workplace. Imprint is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. Imprint welcomes talented individuals from all backgrounds who want to build the future of payments and rewards. If you are passionate about FinTech and eager to grow, let’s move the world forward, together.