[Hiring] Senior Data Scientist, Credit & Fraud Risk Modeling @Kafene

Role Description

Credit and risk are at the heart of our business. We're looking for a Senior Data Scientist, a senior individual contributor who will own the full lifecycle of the ML models that power our credit risk decisions. Reporting directly to the VP of Risk, you'll design, build, deploy, and monitor the models that determine how we approve customers, set credit amounts, predict defaults, and forecast losses. You'll work closely with cross-functional partners across risk, engineering, finance, and sales, and you'll have the rare opportunity to shape both the technical infrastructure and the business strategy behind it.

What You'll Do

• Feature Engineering: Mine internal and external datasets to engineer high-signal features (DTI, PTI, payment behavior, account balance patterns) that improve the predictive power of production credit models.
• Model Development: Own the end-to-end development of strategic credit risk models, including approval amount sensitivity models, credit line optimization, and loss forecasting.
• Data Preparation: Source, clean, and transform messy real-world financial data into modeling-ready datasets.
• Vendor Evaluation: Evaluate third-party data vendors and scoring products, leading cost-benefit analyses to integrate into our models.
• Research & Innovation: Bring in new techniques from the ML literature and apply them to real credit risk problems.
• Model Implementation & Validation: Partner with engineering to get models into production accurately and efficiently, defining standards for model validation.
• Monitoring & Maintenance: Lead recalibration and redevelopment when model performance drifts.
• Compliance & Governance: Navigate model risk governance, regulatory requirements, and data vendor usage policies.
• Cross-Functional Partnership: Translate business questions from risk, finance, and sales into modeling problems and explain solutions in plain language.

Qualifications

• Master's or PhD in a quantitative discipline: Statistics, Mathematics, Data Science, Econometrics, or a related field.
• 5+ years working as a Data Scientist or ML Engineer with a focus on predictive modeling, ideally in credit risk, fraud detection, or financial analytics.
• Advanced Python for statistical modeling and ML.
• Strong SQL for data extraction and feature construction.
• Deep expertise in ML algorithms for structured/tabular data: gradient boosting, ensemble methods, regression models, decision trees, and AutoML frameworks.
• Prior experience in consumer lending, fintech, or financial services is highly preferred.
• Hands-on experience with model risk governance frameworks and working alongside validation teams.
• Ability to communicate complex concepts to diverse audiences.

Requirements

• Experience deploying models that affect real credit or lending decisions.
• Ability to explain a gradient boosting model to a risk committee and a credit policy tradeoff to an engineer.

Benefits

• Salary: Competitive salary range of $95-$140k.
• Healthcare: 80% coverage of medical, dental, and vision insurance costs for you and your dependents.
• Retirement Benefits: 401k plan available from day one.
• Paid Time Off: Flexible paid time off days starting from day one of employment.

Company Description

Kafene is revolutionizing the lease-to-own space, making flexible lease-to-own accessible to everyone. Our team spans NYC headquarters, Wilmington, and remote talent across the globe, united by a culture of collaboration, innovation, and genuine support.