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Company and Job Overview
JAC's client is a BNPL company seeking for a Credit Risk Assessment Manager with a strong background in statistical analysis, data analytics, and anti-fraud strategies. This role is pivotal in leading the assessment of credit risks for private individuals using BNPL services, utilizing advanced statistical techniques and methodologies.
Job Responsibilities
Credit Risk Assessment Strategies for Individuals: Develop and implement effective strategies to assess and manage credit risks for private individuals using BNPL services.
Statistical Analysis and Predictive Modeling for Personal Risk: Utilize strong statistical and data analytics skills to analyze personal financial data, create predictive models, and derive actionable insights for effective individual credit risk assessment.
Anti-Fraud Expertise for Personal Risk Mitigation: Apply deep knowledge of the anti-fraud field to personal risk assessments, contributing to fraud prevention efforts for individual customers.
Personalized Credit Policy Development: Develop and refine credit policies based on statistical findings and anti-fraud measures, focusing on the unique aspects of individual credit risk.
Industry Trends and Best Practices for Personal Risk Management: Stay updated on industry trends, emerging technologies, and best practices in statistical and data analytics for credit risk management and anti-fraud measures, particularly related to personal risk assessment.
Job Requirements
A bachelor’s degree in a relevant field is required.
An advanced degree or professional certification in risk management, or a degree in Statistics, is advantageous.
Proven experience in credit risk assessment with a focus on private individuals, strong statistical analysis, data analytics, and a deep understanding of the anti-fraud field.
Expertise in using statistical models and data analytics tools (R, Python, SAS…) to derive insights, inform credit risk decisions, and enhance anti-fraud measures specifically tailored to personal risk.
Excellent communication skills and the ability to present complex statistical findings to non-technical stakeholders.
Strong leadership skills and the ability to work collaboratively in a team environment.
Fluency in English.