Senior Data Scientist

Get to know the team
GrabFin is an aggregate of FinTech businesses spread across 6 countries in South East Asia, in the Payments, Lending and Insurance domains. Our engineering teams are stationed in Bangalore, Singapore, Indonesia and Vietnam. We are excited to provide financial services to all participants of the Grab Ecosystem be it our Consumers, Drivers or Merchants. Our products are built on fundamental market insights combined with advanced Data Science, Generative AI and engineering to bring the best product market fit across the cross section of our user base. This understanding of our ecosystem combined with world class engineering execution continues to create tremendous value for our customers.
GrabFin's data science team is stationed across Bangalore, Kuala Lumpur and Singapore. We aim to hire a Data Scientist to join our Bangalore office to expand the existing Bangalore team. The data scientist will work in a relatively flat team structure with an independent goal of building and manage critical credit risk analytics models daily. You will be asked to expect to solve hard technical problems and grow into an expert on PD, LGD, and EAD modelling across multiple South East Asian markets. You will have experience with technology, credit risk modelling, and data science along with being. This role is based out of Grab's Bangalore office.
 
The Role:

• Develop PD, LGD, and EAD models across multiple markets (Singapore, Malaysia, Thailand, Philippines, Indonesia and Vietnam), from problem framing to performance measurement.
• Apply a understanding of data science fundamentals and credit risk modelling practices to produce, explainable and stable solutions.
• Build modelling pipelines using traditional and advanced machine learning, including XGBoost, LightGBM, Deep Learning, and evaluate them rigorously.
• Guide model deployment and monitoring: operationalize models, track drift/performance, and support model refresh decisions.
• Communicate with risk, underwriting, and engineering team members to communicate insights through presentations, clear documentation, and decision-ready results.
• Troubleshoot data/model issues and lead structured across datasets, features, and validation results.
• Contribute to best practices in feature engineering, validation, reproducibility, and monitoring Or in a team to solve complex problem statements.
• Individual contributor role with 4+ years of experience expected.
 
The daily activities

• Credit Risk Modelling : Build PD, LGD, and EAD models using machine learning for credit risk assessment. Engineer features from internal data assets to build refined borrower and portfolio profiles.
• Model Development & Validation: Apply both traditional and advanced ML algorithms (Logistic Regression, XGBoost, LightGBM, Random Forest, Deep Learning) with rigorous evaluation and validation methodologies.
• Alternative Data Evaluation: Leverage alternative and non-traditional data sources (e.g., transactional, behavioral, device, bureau, or ecosystem data) to enhance model predictive power, improve risk segmentation, and drive business performance.
• MLOps & Deployment: Build end-to-end pipelines to automate model training, deployment, and monitoring. Track production performance including drift detection and implement feedback loops for continuous improvement.
• Ownership and Collaboration: Design credit risk solutions by working backwards from needs. Lead the delivery of risk models, from concept through production deployment, partnering with risk, underwriting, and engineering partners.