Vice President - SME Risk

Responsibilities:
Strategic Portfolio Analytics and Governance: Direct continuous, granular portfolio analytics to monitor delinquency trends and portfolio health across diverse SME segments. Drive the identification of macro and micro-economic risk drivers, isolating sector-specific performance indicators to balance risk containment with business growth.
Lifecycle SME Credit Strategy: Spearhead the vision, design, and continuous refinement of data-driven credit strategies across the entire SME customer lifecycle. This includes automated acquisition (cash-flow-based underwriting), dynamic limit management and automated early-warning triggers.
Executive Trend Identification and Reporting: Uncover underlying portfolio behaviours and industry-specific macro trends through rigorous statistical validation and complex data analysis. Deliver actionable risk intelligence and strategic guidance to the C-suite and board during leadership portfolio reviews.
Cross-Functional Leadership and Implementation: Drive alignment across Business, Product, and Engineering leadership to map out scalable SME risk strategies, policy rules, and decisioning workflows. Ensure robust, seamless integration of risk engines into the production environment.
Model Optimisation and Scorecard Direction: Partner strategically with the Data Science leadership to guide the development of SME-specific predictive models. Provide crucial domain expertise on key business variables, oversee predictive performance, and dynamically optimise scorecard cut-offs for proprietary risk models (e. g., Bank Statement, GST, and Cash-Flow scorecards).
Alternative Underwriting and Data Innovation: Champion the evolution of underwriting by leveraging both traditional (commercial bureau) and alternative/digital SME data streams (e. g., GST returns, POS/merchant data, supply chain metrics, banking transactions). Innovate optimal configurations to enhance predictive accuracy for thin-file or digitally emerging SMEs.
Product Architecture and P& L Alignment: Maintain a deep functional understanding of SME lending products to ensure risk frameworks and credit policies perfectly align with business margins, unit economics, and product design.
Requirements:
Educational Background: Bachelor's or Master's degree in Computer Science, Engineering, Statistics, Applied Mathematics, Economics, or a highly quantitative discipline from a premier institution.
Professional Experience: 10 to 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.
Technical & Tool Proficiency: 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.
Statistical Expertise: Deep conceptual and practical understanding of advanced statistical foundations, guiding teams in experimental design, hypothesis testing, Bayesian inference, confidence intervals, and probability distributions.
Machine Learning and Data Mining: 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.
Data Dexterity: 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.
Domain Expertise: 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.

Experience
10-14 yrs