Senior Associate - Secured Risk

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Skills you'll use

What you'll do

• This critical role acts as a bridge between technical modeling and business execution. The successful candidate will perform micro-level portfolio analysis, track emerging delinquency patterns, and formulate credit risk strategies. By partnering with Data Science, Product, and Engineering teams, the role ensures that predictive risk models and alternative data streams are optimally deployed to drive safe, profitable asset growth across various secured lending products.

Who can apply

• Educational Background: Bachelor's or Master's degree in Computer Science, Engineering, Statistics, Applied Mathematics, or a highly quantitative discipline from a premier institution
• Professional Experience: 5+ 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.
• Technical & Tool Proficiency: 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.
• Statistical Expertise: Deep conceptual and practical understanding of advanced statistical foundations, including descriptive analytics, experimental design, hypothesis testing, Bayesian inference, confidence intervals, and probability distributions.
• Machine Learning & Data Mining: Proficiency with core machine learning techniques and statistical algorithms, specifically decision tree learning, ensemble methods (Random Forest, Gradient Boosting), logistic regression, and cluster analysis.
• Data Dexterity: Demonstrated competence in processing, clean-up, and engineering of large-scale datasets, with a proven ability to work with both highly structured financial databases and semi-structured/unstructured data sources.
• Domain Expertise: Deep functional knowledge of retail credit lines, secured credit products. Exposure to Fintech lending ecosystems, retail banking, NBFC operations, or SME/LAP/Secured lending is strongly preferred.

About Credit Saison India

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