Risk Manager

Job Description

REQUIREMENTS

• 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

RESPONSIBILITES

• 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

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