Lead Data Scientist
Grab (company site, opens in a new tab)8+ yrsBengaluruOn-siteDirect career pageEst. 30–50 LPA
Posted 2h ago
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Job description
Structured overview of role & requirements
About This Role
- Lead end-to-end development and enhancement of IFRS 9 Expected Credit Loss models for Retail and MSME portfolios covering PD, LGD, EAD components.
- Develop and implement credit risk scorecards and portfolio analytics using Python and SQL to deliver transparent, scalable models suitable for digital banking.
- Provide technical guidance, produce detailed documentation, and support governance, validation, and regulatory audit processes.
Minimum Requirements
- At least 8 years of relevant experience in credit risk modelling or advanced credit analytics in banking, financial services, or consulting.
- Hands-on experience with IFRS 9 ECL modelling including PD, LGD, EAD and financial portfolio scorecards.
- Advanced proficiency in Python (pandas, NumPy, scikit-learn, statsmodels) and strong SQL experience handling complex credit datasets.
- Bachelor's or Master's degree in Statistics, Mathematics, Econometrics, Data Science, Computer Science, Engineering, Finance or a related quantitative field.
Ideal Candidate Profile
- Experienced in managing complete model lifecycle from data prep to deployment and monitoring with governance and regulatory understanding.
- Skilled in communicating complex modelling methodologies and results to both technical and non-technical senior stakeholders.
- Familiar with credit risk modelling techniques (logistic regression, survival analysis, time-series, macroeconomic modelling) and practical application in Retail/MSME banking portfolios.