# Lead Data Scientist at Grab

Grab is hiring a Lead Data Scientist in Bengaluru, India. Level rates it Little AI ●○○○; you can [apply on Grab](https://jobsbylevel.com/go/7a7cff50-69f8-44a2-bed3-adee5e8b9bbe?utm_medium=ai-agent).

AI Level 1, AI centrality 20 out of 100. Bangalore, India.

## Details

- Company: [Grab](https://jobsbylevel.com/companies/grab)
- AI level: AI Level 1 (score 20 out of 100)
- Location: Bangalore, India
- Posted: November 4, 2026
- Apply: https://jobsbylevel.com/go/7a7cff50-69f8-44a2-bed3-adee5e8b9bbe?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=lead-data-scientist-at-grab-3e492a

## Prepare for this job

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## Description

About Grab and Our Workplace Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility. 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. This role primarily focuses on hands-on IFRS 9 ECL modelling, including Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). Additionally, it carries responsibility for credit risk scorecards, portfolio analytics, and model implementation. You will use Python and SQL to develop transparent, scalable and well-governed models suitable for a digital banking environment. 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 PD models, including term structures, point-in-time calibration and the incorporation of forward-looking macroeconomic information. Develop LGD models covering recovery patterns, cure behaviour, workout periods, collateral, costs and discounting. Develop 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 the 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. Experience 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

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Level. https://jobsbylevel.com/jobs/lead-data-scientist-at-grab-3e492a.

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