Machine Learning Engineer

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 the 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 $200 million in sales since inception, we are rapidly growing and looking to expand our team.

We take pride in fostering a dynamic workplace culture that values collaboration, innovation, and mutual support. Our team of 100 is spread between a NYC headquarters, a Wilmington office, and fully remote staff across the country. Last year, we were selected for Built In Startups to Watch and Forbes' Best Startup Employers.

Credit and risk are core pillars of our business, and our expert risk team plays a critical role in shaping competitive approval amounts, rates, and pricing strategies across Kafene’s extensive merchant network.

We are seeking a highly skilled Director or Manager of Machine Learning Engineering to join our growing team. This individual contributor role will have a specific title based on the candidate’s experience and expertise. Reporting to the Vice President of Risk, you will be pivotal in developing, deploying, and monitoring machine learning models that drive our credit risk assessments. These models will encompass cash flow analysis, credit approval and amount determination, default prediction, customer targeting, and loss forecasting.

In this role, you will collaborate closely with cross-functional teams to understand key business challenges and translate them into data-driven, scalable solutions. You’ll own the entire lifecycle of ML model development, from data collection and preprocessing to model training, evaluation, deployment, and ongoing optimization.

Key Responsibilities:

• Feature Engineering: Lead in-depth analysis of internal and external datasets to identify trends and generate actionable insights. Develop innovative features, such as Debt-to-Income (DTI), Payment-to-Income (PTI), historical payment behavior, and account balance data, that capture and predict credit trends effectively.
• Data Manipulation: Expertly manipulate, clean, and transform both structured and unstructured data to prepare it for robust modeling. Ensure data integrity and readiness for machine learning applications.
• Model Development: Take a central role in designing and developing strategic models. This includes creating approval amount sensitivity models to optimize line assignment strategies and drive business outcomes.
• Vendor Evaluation: Collaborate with various external data vendors to evaluate third-party data and scoring products. Conduct comprehensive cost-benefit analyses to inform decisions on vendor partnerships and data integration.
• Research and Implementation of Advanced Techniques: Stay ahead of the curve by researching and incorporating the latest machine learning algorithms and feature engineering techniques into model development. Continuously enhance the performance of credit risk models through innovative approaches.
• Model Compliance: Stay informed on emerging regulatory requirements and industry trends to ensure all models adhere to best practices in compliance. Ensure models comply with data vendors' permissible usage policies and regulatory standards.
• Model Implementation and Validation: Work closely with technology and engineering teams to accurately implement and validate models. Identify and introduce new processes to improve the speed and accuracy of model implementation.
• Model Monitoring and Maintenance: Proactively monitor model performance, identifying any issues or deterioration. Lead efforts in calibrating and redeveloping risk models to maintain their accuracy and reliability. Develop and manage regular monitoring processes to ensure models remain effective and aligned with business goals.
• Model Documentation and Governance: Ensure all credit risk models are thoroughly documented, including development processes, assumptions, methodologies, and validation procedures. Contribute to model governance initiatives and support internal and external audit processes as required.
• Cross-Functional Collaboration: Collaborate closely with teams across risk management, technology, engineering, finance, and sales to gather data, understand business requirements, and align credit risk modeling efforts with broader business objectives. Support the seamless integration of credit risk models into the company's risk management framework.

Core Skills and Qualifications:

• Advanced Degree: A Master’s or PhD in a highly quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related discipline is preferred.
• Experience: 3+ years of hands-on experience as a Data Scientist or Machine Learning Engineer, with a strong focus on developing and deploying machine learning models in areas such as credit risk, fraud detection, or marketing analytics.
• Technical Requirement: Advanced proficiency in Python, with a proven track record of developing complex machine learning models.
• Technical Requirement: Strong SQL skills are essential for efficient data manipulation and querying.
• Technical Requirement: In-depth knowledge of machine learning algorithms and techniques, including decision trees, regression models, gradient boosting machines, ensemble methods, AutoML tools, etc.
• Model Governance: Experience with model risk governance frameworks and collaboration with model validation teams to ensure compliance with industry standards and regulatory requirements is a plus.
• Industry Knowledge: Previous experience in the lending or financial services industry is highly preferred, particularly in the context of credit, fraud, marketing, or other financial applications.
• Communication Skills: Ability to effectively communicate complex technical information, both verbally and in writing, to diverse audiences, including technical peers, non-technical stakeholders, and executive leadership.

Compensation and Benefits:

• Base Salary: Earn a competitive base salary ranging from $130,000 to $190,000.
• Healthcare: We prioritize your well-being by covering 80% of medical, dental, and vision insurance costs, including coverage for your spouse, children, and other dependents.
• Retirement Benefits: Begin planning for your future from day one with our 401k plan.
• Paid Time Off: We understand the importance of work-life balance. That's why we offer flexible paid time off days starting from day one of your employment.

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 jobs@kafene.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.