Senior Data Scientist (Business & Transaction Platform)

Get to Know the Team

The Business & Transaction Platform (BTP) Data Science team develops scalable machine learning algorithms and intelligent platforms that power transaction efficiency and platform reliability across Grab. We solve core challenges across prediction, forecasting, classification, and generative AI—building foundational data products that support downstream applications across the Grab ecosystem in Southeast Asia.

Get to Know the Role

We are looking for a Senior Data Scientist to help construct in-house machine learning platforms and deploying production-grade ML and LLM solutions to solve complex business problems at scale. You will apply cutting-edge technologies to drive real-world impact for millions of consumers and merchant partners.Reporting to the Senior Data Science Manager, this is a full-time individual contributor role based onsite at our One North office in Singapore.Ready to shape intelligent platform systems at regional scale? Join us and apply today.

The Critical Tasks You Will Perform

• Architect Scalable ML Solutions: Design, develop, and benchmark efficient deep learning and machine learning algorithms for large-scale prediction, classification, and forecasting problems.
• Deploy Production LLMs and Pipelines: Build, fine-tune, and deploy state-of-the-art LLMs and ML pipelines into production platforms in collaboration with engineering teams.
• Process & Feature Engineering: Analyse massive, multi-modal datasets and build data processing pipelines using distributed computing frameworks.
• Measure Business Impact: Design and execute offline evaluations and online A/B experiments to validate model accuracy, latency, and business performance metrics.
• Drive Platform Innovation: Stay at the forefront of machine learning and GenAI research, contributing to technical IP, prototypes, and team best practices.
• Cross-Functional Integration: Partner with Product, Engineering, and Operations stakeholders to translate business requirements into scalable ML features.
• Leverage AI for Productivity: Utilize GenAI tools and modern development workflows to accelerate code delivery, pipeline testing, and technical documentation.

What Essential Skills You Will Need

• Education & Domain Expertise: A Master's or PhD in Computer Science, Operations Research, Mathematics, Data Science, or a related quantitative field.
• Relevant Track Record: At least 2 years of hands-on industry experience building, deploying, and monitoring machine learning or deep learning models in production environments.
• Technical & Coding Proficiency: Programming skills in Python and PyTorch, alongside practical experience with SQL and Spark for distributed data processing.
• End-to-End Pipeline Knowledge: Understanding of data extraction, transformation, and pipeline engineering (ETL) within large-scale data platform architectures.
• LLM & Deep Learning Experience: Hands-on experience developing or fine-tuning transformer models, NLP architectures, or modern LLM frameworks.
• Stakeholder & Analytical Translation: Demonstrated ability to translate ambiguous business and transaction challenges into concrete, measurable ML formulations.
• Mandatory AI Productivity: Experience engaging with AI tools and emerging technologies to enhance productivity, improve workflows, and contribute new ideas.