Sr Associate Data Science

Job Responsibilities:

Conduct both qualitative and quantitative evaluations of multiple performance metrics for the loan portfolio.
Create a credit risk model and execute various projects, including but not limited to computing expected credit loss (ECL), developing an early warning system (EWS) rating methodology, conducting stress testing, and enhancing credit decision-making processes
Prepare a comprehensive report documenting the analysis, methodology, and findings, and deliver a presentation to internal stakeholders.
Design, develop, and sustain scorecards and dashboards that offer timely insights on the performance of portfolio risks.
Improve the existing system's risk monitoring reports and fulfill ad-hoc data requests from various stakeholders.
Oversee the aggregation and consolidation of data from various sources across multiple systems, which will be utilized for risk dashboards and machine learning mode
Utilize both internal and external data sources to address business challenges across various domains (such as underwriting and risk monitoring) in a dynamic setting, incorporating both conventional and non-traditional data sources.

Job Requirements:

A Master's or Bachelor's degree in Business Administration, Statistics, or a related field
A minimum of 2 years of practical experience in deploying statistical and machine learning algorithms (such as regression and decision trees), along with proficiency in conducting statistical analyses using Python or R.
Areas of expertise/competencies: Cluster analysis, Data analysis, Financial modeling, Logistic regression, Machine learning, Risk analysis, Scorecard development, Statistical modeling.
Technical Skills: Python/R, SQL, Power-BI