Responsibilities:

• Develop and analyze collections and receivables management processes to optimize recovery rates and minimize credit losses and delinquency risk.
• Calculate, monitor, and analyze key performance indicators (KPIs) related to collections effectiveness and portfolio health.
• Collaborate closely with Operations, Product, Data, and Risk teams to enhance collections strategies and improve business outcomes.
• Evaluate various business scenarios and provide data-driven recommendations to support strategic decision-making.
• Document methodologies, assumptions, models, and analytical findings in a clear and transparent manner for management and key stakeholders.
• Continuously improve analytical insights using new data sources and operational feedback.

Qualifications & Requirements:

• Strong understanding of finance, cash flow analysis, time value of money, and credit risk management concepts.
• Solid knowledge of statistics, probability, predictive modeling, and forecasting techniques.
• Experience working with large datasets and transforming data into actionable business insights.
• Proficiency in data analysis and modeling tools, including Python, SQL, Advanced Excel, and Business Intelligence (BI) platforms.
• Familiarity with machine learning techniques and their application in financial services is considered an advantage.

Industry Experience:

• Experience in LendTech, FinTech, digital lending, credit scoring, collections, or risk management environments.
• Understanding of customer lifecycle analytics, portfolio monitoring, and collections optimization methodologies.

• Strong analytical thinking and problem-solving capabilities.
• Excellent presentation and communication skills, with the ability to explain complex analytical concepts to both technical and non-technical audiences.
• High emotional intelligence and strong stakeholder management skills.
• Ability to document analyses, methodologies, and results clearly and effectively.
• Strong attention to detail and commitment to continuous improvement.
  

  Education:

  • Bachelor's or Master's degree in Financial Engineering, Industrial Engineering, Economics, Statistics, Mathematics, Data Science, or a related quantitative discipline.