Data Analysts specializing in Micro Data Analytics develop and implement risk models based on detailed data collected from supervised banking entities. They perform comprehensive data analysis, model development, validation, and deployment. The role involves identifying compliance issues through data analysis, designing data templates for risk assessment, and providing critical insights for banking supervision and regulation.

Development of Risk models based on micro data from supervised entities Periodic model updates and improvements Gather and prepare data from various sources for modeling Clean and pre-process data: handle missing values, outliers, inconsistencies Train models using training sets, evaluate accuracy using test sets Use cross-validation techniques to ensure model generalizability Assess model performance based on problem type Optimize models through hyperparameter tuning, feature selection, regularization Model deployment in production environment Identify compliance issues in CBS systems of supervised entities Design data templates for compliance issues and best industry practices Work with data engineers and software developers on model integration

Master's degree in Statistics/Applied Statistics/Economics/Econometrics/Applied Economics/Quantitative Economics/Finance/Quantitative Finance/Commerce Good knowledge of data analytical tools/technologies: Python, R, SQL, etc. (certificate/proof required) Minimum 5 years of experience as Data Analyst at financial institution/IT Firm/Consultancy (excluding research/internship period) Out of 5 years minimum, at least 3 years in credit risk modeling with AI/ML techniques in commercial bank/large financial company Desirable: AI/ML certification courses, Data Science certifications, PhD with experience in data analytics to economic/financial analysis