Risk Manager

The risk team is a fundamental pillar for our business. As a Risk Manager, you will play an important role in driving healthy business growth for Kafene. You will conduct statistical analyses to develop top-tier retailer merchant risk strategies that balance loss control and strong partnerships. You will learn, innovate, and grow alongside an innovative team that strives for excellence.

What You’ll Do

Leverage multiple, complex data sources such as third-party vendor data, internal performance metrics, and machine learning models to perform quantitative and decision-tree analyses to produce actionable recommendations for new merchant underwriting and assess existing merchant profitability to support healthy business growth.

Monitor, investigate, and conduct p &l analysis to optimize merchant underwriting policy and processes to improve merchant experience while controlling merchant risk at scale with a strong sense of ownership and urgency; ongoing collaboration to enable merchant risk operation teams to handle edge cases

Design and maintain Sigma reporting and conduct statistical analyses to track merchant engagement and performance across different dimensions to ensure healthy growth and profitability

Partner cross-functionally (clear communication + strong collaboration) with sales and operations teams to deliver customized merchant treatments that ensure profitability with smooth sales operations

Investigate merchant fraud cases, identify relevant patterns, and implement preventive measures to mitigate risk

Learn and leverage analytical tools (Python/R, SQL, etc.) to perform statistical and decision-tree analyses to identify root causes and potential business opportunities.

Partner with data and engineering teams to enhance treatment capabilities and monitor implementation quality

Communicate findings clearly through summaries, presentations, and process documentation

What You’ll Bring

Master’s degree in a quantitative discipline such as Statistics, Operations Research, Economics, Engineering, Data Science, or any other STEM major

2+ years of work experience in LTO or the financial industry

High-level proficiency in Python, SQL, R, or other data mining and analytical tools

Strong sense of ownership, timeline, and collaboration

Prior experience in the lease-to-own (LTO) or the financial lending industry, with good P&L analytical experience

Familiarity with any decision-tree analysis approach, like pivot tables, Knowledge Seeker, or Enterprise Miner