全职 Senior Data Scientist (GrabFin) 工作, 薪水, Grab 公司招聘中 - Ricebowl

Get to Know the Team

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.

What Essential Skills You Will Need

• At least 4 years of experience building ML models in production environments — You will deploy models that directly impact lending decisions, requiring the ability to move models from experimentation to production.
• You are proficient in Python programming and ML/DL libraries (scikit-learn, XGBoost, TensorFlow or PyTorch) — You will write and maintain code for model development, pipeline automation, and deployment, and select appropriate algorithms for credit risk modeling and customer prediction tasks.
• Experience with distributed data processing (Spark, SQL) — You will build pipelines handling large-scale financial data across multiple markets, requiring the ability to process data across distributed systems.
• You have experience with feature engineering and model evaluation metrics — You will create features from raw transaction and behavioural data, and select metrics that measure model performance for financial applications.
• You have experience with multi-agent systems using frameworks like LangGraph, CrewAI, or ADK — You will implement agent-based systems for complex decision-making workflows using patterns such as ReAct, self-reflection, or hierarchical delegation.
• You can explain model behaviour to non-technical stakeholders — You will present to product managers and leadership, requiring you to communicate technical concepts in accessible language and connect model outputs to business outcomes.
• You have experience engaging with AI tools and emerging technologies to enhance productivity, improve workflows, and contribute new ideas.