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Requirements:
Bachelor's or Master's degree in Computer Science, Engineering, Statistics, Applied Mathematics, or a highly quantitative discipline from a premier institution
9-14 years of professional experience within data science, risk analytics, or quantitative risk management. Proven experience building predictive models, optimizing credit policies, and delivering complex analytical insights.
Advanced mastery of SQL for complex data extraction, querying, and manipulation. Strong hands-on programming proficiency in Python or R for statistical analysis and machine learning.
Deep conceptual and practical understanding of advanced statistical foundations, including descriptive analytics, experimental design, hypothesis testing, Bayesian inference, confidence intervals, and probability distributions.
Proficiency with core machine learning techniques and statistical algorithms, specifically decision tree learning, ensemble methods (Random Forest, Gradient Boosting), logistic regression, and cluster analysis.
Demonstrated competence in processing, cleanup, and engineering of large-scale datasets, with a proven ability to work with both highly structured financial databases and semi-structured/unstructured data sources.
Deep functional knowledge of retail credit lines and secured credit products. Exposure to fintech lending ecosystems, retail banking, NBFC operations, or SME/LAP/secured lending is strongly preferred.
Experience
8-12 yrs
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