Senior Data Scientist (GrabFin)

Financial Services (FS) brings together FinTech and Banking businesses across 6 countries in Southeast Asia, covering Lending, Payments, and Insurance. You will join a team building innovative financial services for drivers, consumers, and merchants within the Grab ecosystem. The team combines market insights with data science and engineering to develop products that fit real user needs. You will work in a flat structure with ownership over your models and solutions, focusing on both batch and real-time data science applications.

Get to Know the Role

You will build and deploy production-grade machine learning systems for FS Lending products serving drivers, passengers, and merchants. You will develop predictive models using machine learning and deep learning techniques, create data pipelines, and validate model performance on real-world datasets. You will work with product managers, engineers, and data scientists to translate business requirements into ML solutions.

You'll report into the Principal Data Scientist and work onsite in Grab's One North Singapore office. Ready to make an impact? Apply now to join our team!

The Critical Tasks You Will Perform

• You will design, build, and deploy agentic systems and GenAI workflows (using frameworks like LangGraph or CrewAI) to solve complex decision-making problems alongside core predictive ML models, enhancing overall engineering productivity.
• You will build and deploy scalable ML models using Python, Spark, and cloud-native tools to predict lending outcomes and customer behaviour.
• You will develop data pipelines and feature stores to support model training and inference, ensuring data flows correctly from source to production.
• You will engineer predictive features from internal data assets and identify external data sources to incorporate into model development.
• You will validate model performance on real-world datasets and lead model refresh cycles when you detect performance drifts or gaps.
• You will present model findings to senior leadership, explaining risk trade-offs and translating insights into strategic recommendations for policy changes, pricing adjustments, or customer targeting strategies.