RequirementsBachelor's degree in finance, Economics, Data Science, Statistics, Business, Operational research or a related field.Master's degree in a relevant field or professional certifications is a plus3-5 years of experience in credit risk collections management, preferably within the SME sectorProven track record of improving collections outcomes and operational efficienciesExperience with collections and recovery strategies is essentialHands-on experience in data analytics, statistical modelling, and risk scoringProficiency in data analysis tools (e.g., SQL, R, Python)Experience with data visualization tools (e.g., Tableau, Power BI)Strong Excel skills for financial modeling and analysisIn-depth knowledge of credit bureau data, scoring methods, and financial regulations related to collectionsResponsibilities Conduct a thorough analysis of the SME portfolio to identify potential risks and trendsDevelop and implement collection strategies based on data-driven insights to minimize delinquency and improve recovery ratesMonitor execution and performance of strategies and ensure that strategies are amended and kept up to dateDevelop and maintain predictive models for collections to identify early warning signs and take preventative measuresRegularly monitor the SME portfolio performance, identifying risk triggers, delinquency trends, and opportunities for improvementProvide actionable insights and recommendations to the Data Science and Operational Collections teams to mitigate risksDevelop and maintain dashboards and reports to track key performance metrics (KPIs), delinquency, and recovery ratesAnalyse large datasets using statistical methods to inform credit risk and collections strategiesCollaborate with the Data Science Manager to improve the accuracy and predictive power of credit risk modelsSupport the Collections team by providing data-driven insights for operational decision-making