SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Kafene is a fintech company revolutionizing the lease-to-own space with AI-powered credit decisioning. The company has processed over $500 million in originations and serves prime and non-prime customers across furniture, appliances, electronics, tires, and durable goods.
You will join as Manager of Data Science, Credit & Fraud Risk Modeling, reporting directly to the VP of Risk. This is a senior individual contributor role with people management responsibilities, owning the full lifecycle of machine learning models that power credit risk decisions at Kafene.
Key responsibilities include:
- Feature Engineering: Mine internal and external datasets to engineer high-signal features (DTI, PTI, payment behavior, account balance patterns) that improve predictive power of production credit models.
- Model Development: Own end-to-end development of strategic credit risk models including approval amount sensitivity, credit line optimization, and loss forecasting.
- Data Preparation: Source, clean, and transform real-world financial data into modeling-ready datasets, setting standards for data integrity.
- Vendor Evaluation: Evaluate third-party data vendors and scoring products, leading cost-benefit analyses for integration decisions.
- Research & Innovation: Apply new ML techniques from literature to real credit risk problems, shipping improvements to production.
- Model Implementation & Validation: Partner with engineering to deploy models accurately and efficiently, defining validation standards.
- Monitoring & Maintenance: Monitor model performance in production, leading recalibration and redevelopment when performance drifts.
- Compliance & Governance: Navigate model risk governance, regulatory requirements (SR 11-7), and data vendor policies.
- Cross-Functional Partnership: Translate business questions from risk, finance, and sales into modeling problems and communicate solutions clearly.
Required qualifications:
- Master's or PhD in quantitative discipline (Statistics, Mathematics, Data Science, Econometrics, or related field)
- 5+ years as Data Scientist or ML Engineer with 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, decision trees, AutoML
- Prior experience in consumer lending, fintech, or financial services
- Hands-on experience with model risk governance frameworks
- Strong communication skills for both technical and business audiences
Kafene's 175-person team is based in NYC headquarters with offices in Wilmington and remote talent globally. The company has been recognized by Built In as a Startup to Watch and by Forbes as one of America's Best Startup Employers.
