# Lead Data Scientist **Company**: Grab **Location**: Bangalore, India **Experience**: senior **Job type**: full-time **Category**: Data Science **Industry**: Finance **Apply**: https://jobs.smartrecruiters.com/Grab/744000159596270-lead-data-scientist?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_32d9a3eb-80a ## Description We are looking for an experienced credit risk modeller to lead the development of Expected Credit Loss (ECL) models for our Retail and MSME portfolios. Key Responsibilities: - Lead the end-to-end development and enhancement of IFRS 9 ECL models across Retail and MSME portfolios. - Develop 12-month and lifetime Probability of Default (PD) models, including term structures, point-in-time calibration, and incorporation of forward-looking macroeconomic information. - Develop Loss Given Default (LGD) models covering recovery patterns, cure behaviour, workout periods, collateral, costs, and discounting. - Develop Exposure at Default (EAD) and credit conversion factor models for term loans and revolving credit facilities. - Design and enhance ECL methodologies covering portfolio segmentation, default definitions, increase in credit risk, staging, and multiple economic scenarios. - Develop application, behavioural, and collection scorecards using traditional statistical methods and machine-learning techniques. - Perform model calibration, back-testing, benchmarking, sensitivity analysis, stress testing, and ongoing performance monitoring. - Translate modelling methodologies into modular and reproducible Python implementations. - Build reliable analytical datasets and model pipelines using Python and SQL, working with Data Engineering and Technology teams. - Produce comprehensive model documentation and support independent validation, audit, governance committees, and regulatory reviews. - Partner with Credit Risk, Finance, and Business teams to explain ECL movements, portfolio trends, and financial impact of model changes. - Contribute to the Bank's broader credit risk modelling, portfolio optimisation, and capital-management frameworks. - Provide technical guidance and mentorship to other modellers and analysts. QUALIFICATIONS: - At least eight years of relevant experience in credit risk modelling, quantitative risk, data science, or advanced credit analytics within banking, financial services, or consulting. - Hands-on experience developing IFRS 9 ECL models, with demonstrated expertise in PD and practical experience across LGD and EAD modelling. - Experience with ECL concepts, including 12-month and lifetime expected losses, staging, significant increase in credit risk, and forward-looking macroeconomic scenarios. - Experience developing and monitoring credit risk scorecards for Retail, MSME, or similar lending portfolios. - Advanced proficiency in Python for statistical modelling and data analysis, using libraries such as pandas, NumPy, scikit-learn, and statsmodels. - Strong SQL experience working with large, complex, and longitudinal credit datasets. - Knowledge of relevant modelling techniques, including logistic regression, survival analysis, transition matrices, time-series or macroeconomic modelling, and recovery analysis. - Experience managing the complete model lifecycle, including data preparation, methodology development, implementation, monitoring, documentation, and remediation. - Sound understanding of model risk governance, validation standards, and regulatory expectations. - Ability to communicate complex methodologies and modelling results clearly to senior, technical, and non-technical stakeholders. - Bachelor's or Master's degree in Statistics, Mathematics, Econometrics, Data Science, Computer Science, Engineering, Finance, or another quantitative discipline. Good to Have: - Experience with Basel credit risk models, IRB models, stress testing, or capital modelling. - Experience deploying models into production and working with Git, automated testing, CI/CD, or model-monitoring frameworks. - Familiarity with AWS services and cloud-based data or modelling platforms. - Experience in digital banking, fintech, or portfolios with limited historical data. - Professional qualifications such as FRM, CFA, Chartered Banker, or an equivalent risk qualification. ADDITIONAL INFORMATION: Life at Grab We care about your well-being at Grab, here are some of the global benefits we offer: - We have your back with Term Life Insurance and comprehensive Medical Insurance. - With GrabFlex, create a benefits package that suits your needs and aspirations. - Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave. - We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges. ## Skills ### Required - Python - SQL - credit risk modelling - data science - statistics - machine learning - data analysis - pandas - NumPy - scikit-learn - statsmodels - logistic regression - survival analysis - transition matrices - time-series modelling - macroeconomic modelling - recovery analysis ### Nice to have - Basel credit risk models - IRB models - stress testing - capital modelling - Git - automated testing - CI/CD - model-monitoring frameworks - AWS services - cloud-based data or modelling platforms - digital banking - fintech - FRM - CFA - Chartered Banker --- Source: [Apply at jobs.smartrecruiters.com](https://jobs.smartrecruiters.com/Grab/744000159596270-lead-data-scientist?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)