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Salary: USD 95,000 - 130,000 / annual
Kafene is a fintech company revolutionizing the lease-to-own space with AI and machine learning. The company has processed over $500 million in originations and serves retailers across furniture, appliances, electronics, tires, and durable goods. With 150 employees based in NYC headquarters, Wilmington, and remote locations, Kafene has been recognized as a Startup to Watch by Built In and one of the Best Startup Employers by Forbes.
The Risk team is a key growth driver for the business. As Risk Manager, Collections, you will report to the VP of Risk and help build Kafene's profit-driven consumer underwriting strategies. You will work with complex data sources and machine learning models, partnering across technology and senior leadership to develop strategies that optimize credit risk, fraud risk, and other factors across underwriting, portfolio management, and collections.
Key responsibilities include:
- Leverage multiple complex data sources (credit bureau information, alternative data, consumer behavior, macro environment, portfolio performance metrics) to perform quantitative analyses, classification/decision-tree analyses, and machine learning modeling to identify root causes and business opportunities.
- Translate data into meaningful insights and design, implement, and iterate on strategies to drive profitable business growth. Present findings to senior management, including the CRO and CEO.
- Design and manage A/B tests, pilot programs, and controlled experiments to evaluate new strategies, including defining test populations, randomization logic, and success criteria.
- Build and maintain dashboards and reporting infrastructure to enable real-time monitoring of strategy performance across key metrics.
- Monitor acquisition and portfolio performance related to consumer/merchant credit risk, fraud risk, lease amount, term, channel, and other important metrics across the account lifecycle.
- Monitor internal and macroeconomic performance to define risk appetite and maintain business health across economic environments.
- Partner with technology, product, and cross-functional teams to ensure timely, quality implementation with diligent test validation and performance tracking.
Requirements:
- Master's degree in a quantitative discipline such as Statistics, Operations Research, Economics, Engineering, Data Science, or other STEM majors.
- Minimum 2 years in the financial industry in an analytical function. Experience in credit risk strategy/collections areas is a plus.
- High proficiency in business reporting and analytical tools such as Power BI and Tableau (required).
- High-level proficiency in Python, SQL, SAS, or other data mining and analytical tools. Familiarity with decision-tree analysis tools like Enterprise Miner is a plus.
- Experience extracting business insights from data analysis and turning them into clear recommendations in the financial services/consumer lending industry.
