Data Scientist Senior Associate
Commonwealth Bank of Australia (company site, opens in a new tab)1–3 yrsBengaluruOn-siteDirect career pageEst. 10–18 LPA
Posted 12h ago
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Reach people at Commonwealth Bank of Australia
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Commonwealth Bank of Australia you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Lead development, maintenance, and enhancement of key credit risk models for credit risk RWA and provisioning across all lending businesses in the CBA Group.
- Generate insights via advanced statistical and econometric modeling (Time Series, Macroeconomic, Non-linear Regression) using R and Python to analyze credit portfolios.
- Collaborate with multiple teams (model validation, project teams, enterprise services, business unit risk teams) to support model lifecycle, including data preparation and impact analysis on Expected Loss, capital, and Risk Weighted Assets.
Minimum Requirements
- 1-3 years of relevant experience in credit risk modeling or advanced analytics.
- Bachelor's, Master's, or PhD in Statistics, Mathematics, Data Science, Computer Science, or related field.
- Proficiency in R or Python for modeling; experience with Teradata SQL or Microsoft SQL is advantageous.
- Familiarity with Basel regulatory standards, APRA credit risk regulations, or IFRS9 collective provisions is an advantage but not mandatory.
Ideal Candidate Profile
- Experience working within credit risk model development focusing on Probability of Default, Loss Given Default, and Exposure at Default models.
- Ability to work independently with minimal supervision and deliver error-free, well-documented modeling results to diverse stakeholders.
- Comfortable interfacing with multiple cross-functional teams including data, validation, and business risk units in a matrix organizational structure.