Kafene is a leading point-of-sale financing partner dedicated to empowering flexible ownership solutions for underserved customers nationwide. By enabling our retail partners to offer flexible lease-to-own (LTO) purchase options for prime and nonprime consumers, Kafene helps merchants grow their customer base, and meet growing demand for furniture, appliances, electronics, tires and other durable goods. Utilizing more than 20,000 data inputs in tandem with cutting-edge AI and machine learning technologies, our platform creates a best-in-class experience for both merchants and customers. With over $100 million in sales since inception, we are rapidly growing and expect to double our scale in the next 12 months.
Our team values and celebrates our culture, and we are proudly selected as one of the Built-In Startups to Watch, Lendit Industry award finalist, and Forbes' Best Startup Employers.
Feature Engineering: Conduct in-depth analysis of internal and external datasets to identify trends, and generate insights to inform the creation of new features (like DTI, PTI, historical payment, account balance data, etc.) that can capture credit trends.
Data Manipulation: Manipulate, clean, and transform structured and unstructured data for modeling purposes.
Model Development: Actively participate in strategy design and development, for example, developing approval amount sensitivity models to further optimize line assignment strategy.
Vendor Evaluation: Work with different credit risk data vendors to conduct cost benefit analyses for 3rd party vendor data and score products.
Research Up-to-Date Machine Learning Techniques: Learn and incorporate latest machine learning algorithms and feature engineering techniques into modeling development to continuously improve the performance of credit risk models.
Model Compliance: Keep abreast of current and emerging regulatory requirements, industry trends, to ensure best practices in model compliance framework. Be compliant with data vendors’ permissible usage and regulatory requirements.
Model Implementation and Validation: Work cross-functionally with the tech and engineering department to accurately implement and validate models. Identify new processes and practices to continuously improve model implementation speed and accuracy.
Model Monitoring and Maintenance: Monitor model performance and residues, and calibrate risk models to ensure their accuracy and reliability. Develop regular monitoring and residue assessment processes to identify model production issues and model performance deterioration, thereby driving timely model recalibration and redevelopment. Proactively incorporate changes into existing models and recommend adjustments to risk management strategies as needed.
Model Documentation and Governance: Maintain comprehensive documentation of credit risk models. Contribute to model governance initiatives and assist in internal and external audits as required.
Cross-Functional Collaboration: Work closely with risk management, tech and engineering, finance, and sales, to support the integration of credit risk models into broader risk management frameworks.
Core Skills and Qualifications:
Advanced Degree (Masters or PhD preferred) in Statistics, Mathematics, Operations Research, Economics, Financial Engineering, Mathematical Finance, Industrial Engineering, Data Science, and other highly quantitative disciplines.
3-5 years of prior experience as a Data Scientist or a ML Engineer with hands on risk modeling and machine learning required
Advanced knowledge of Python and strong proficiency in SQL are must.
In-depth understanding of common machine learning algorithms/techniques such as decision tree, regression, gradient boosting machines, ensemble, AutoML, etc.
Knowledge of model risk governance and experience collaborating with model validation teams to ensure compliance required.
Prior work experience in credit risk modeling and model compliance in the lending / financial industry preferred.
Able to communicate technical information verbally and in writing to both technical and non-technical audiences.
Compensation: $130,000 - $160,000
Kafene is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other legally protected characteristics. If you are applying for a job in the U.S. and need a reasonable accommodation for any part of the employment process, please send an e-mail to email@example.com and let us know the nature of your request and contact information. Please note that only those inquiries concerning a request for reasonable accommodation will be responded to from this email address.