Company
Risk Analytics
Intern-RiskAnalytics
Neural analysis suggests this role is
optimal for Entry candidates.
“Intern - Risk Analytics. Skills: risk analytics, credit risk modelling, Python, SQL. Automate risk analytics existing processes like provisioning, stress testing etc. and improve code efficiency. Perform exploratory data analysis (EDA)”
Industry & Context.
What They're Looking For.
Must Have
Final year student or recent graduate in a quantitative field (Engineering, Mathematics, Statistics, Economics, etc.) from Top colleges, programming skills in Python/R/SAS (Must have), familiarity with SQL, Good understanding of data manipulation libraries (Pandas, NumPy), basic statistics
Nice to Have
Exposure to machine learning or statistical modelling, Experience working on projects involving real datasets (academic or personal)
What You'll Do.
Automate risk analytics existing processes like provisioning
stress testing etc. and improve code efficiency
Perform exploratory data analysis (EDA)
Support the team in developing
and validating credit risk models (PD/LGD/EAD) including data extraction
and transformation using Python and SQL
Assist in model validation
monitoring and performance tracking
How You'll Work.
Team & Collaboration
Collaborate with team members on ongoing projects and contribute to discussions
Full Job Description
## Roles and Responsibilities Automate risk analytics existing processes like provisioning, stress testing etc. and improve code efficiency Perform exploratory data analysis (EDA) Support the team in developing, testing, and validating credit risk models (PD/LGD/EAD) including data extraction, factor analysis, and transformation using Python and SQL Assist in model validation, monitoring and performance tracking Collaborate with team members on ongoing projects and contribute to discussions ## What are we looking for Final year student or recent graduate in a quantitative field (Engineering, Mathematics, Statistics, Economics, etc.) from Top colleges. Strong programming skills in Python/R/SAS (Must have) and familiarity with SQL Good understanding of data manipulation libraries (Pandas, NumPy) and basic statistics Attention to detail and eagerness to learn Good to Have (Not Mandatory) Exposure to machine learning or statistical modelling Experience working on projects involving real datasets (academic or personal) What You’ll Gain Opportunity to build production-level coding skills Hands-on experience working with real financial datasets Exposure to credit risk modelling and model lifecycle processes Mentorship from experienced risk analytics professionals
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