About the role
Requirements:
- Bachelor's or master's degree in computer science, engineering, statistics, or applied mathematics.
- 4-7 years of professional experience in 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, including decision tree learning, ensemble methods (Random Forest and Gradient Boosting), logistic regression, and cluster analysis.
- Demonstrated competence in processing, cleaning, and engineering large-scale datasets, with the ability to work with both highly structured financial databases and semi-structured or 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.