VP - Risk Analytics

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, Statistics, Applied Mathematics, Economics, or a highly quantitative discipline from a premier institution.
• 12+ years of progressive experience within Risk Analytics, SME Risk Management, or Quantitative Credit Policy.
• Proven track record in a leadership capacity, successfully building predictive models, optimising SME credit policies, and driving measurable P& L impact.
• Strong architectural understanding and advanced mastery of SQL for complex data extraction and manipulation.
• Hands-on programming proficiency (or strong ability to lead technical teams) in Python or R for statistical analysis and machine learning.
• Deep conceptual and practical understanding of advanced statistical foundations, guiding teams in experimental design, hypothesis testing, Bayesian inference, confidence intervals, and probability distributions.
• Strong conceptual proficiency with core machine learning techniques and statistical algorithms applied to credit risk, specifically decision trees, ensemble methods (Random Forest, Gradient Boosting), logistic regression, and cluster analysis.
• Demonstrated competence in overseeing the processing, clean-up, and engineering of large-scale datasets, with a proven ability to derive insights from both structured financial databases and semi-structured alternative SME data sources.
• Deep functional knowledge of commercial credit lines, SME/MSME lending ecosystems, and cash-flow underwriting.
• Extensive exposure to Fintech lending, B2B credit, retail banking, NBFC operations, or Supply Chain Finance is strongly preferred.