Risk Manager
New
K
KafeneFintech
Remote...remote talent across the globeFull-TimeManager
Salary95,000 - 140,000 USD per year
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Job Details
- Experience
- 2+ years of work experience
- Required Skills
- PythonSQLData MiningR
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 table, Knowledge Seeker or Enterprise Miner
Responsibilities
- 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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