Senior Data Scientist at Grab in Bengaluru, Karnataka, India

companyDescription 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. jobDescription 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. qualifications The Essential Skills You Need Core ML & Credit Risk: 4+ years of experience building PD, LGD, and EAD models (development, and iteration). High proficiency in Python, SQL and PySpark with full understanding of data science fundamentals (statistics, model validation, feature engineering, evaluation metrics, data quality, model monitoring, and experiment methodology). Machine Learning Expertise: Comfort with both traditional and advanced ML algorithms including XGBoost, LightGBM, Random Forest MLOps & Productionization: Experience with model deployment and monitoring, including ongoing performance tracking and support for model governance processes. Partner Management: ability to work in a partner environment, including presenting results, aligning on requirements, and translating analysis into applicable decisions. Engineering Excellence: with a structured approach to and risk-related constraints, with the ability to balance model performance with business trade-offs. additionalInformation 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. What we stand for at Grab We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.