Principal Data Scientist (User Understanding Platformisation)

You will join our Search and Personalization team — a group of engineers and data scientists building the intelligence that helps millions of users discover and engage with food, groceries, mobility, and services across Southeast Asia. We work with engineering, product, and business teams to develop foundational machine learning capabilities that allow Grab to better understand users and deliver relevant experiences across the platform.

Get to Know the Role

Reporting to the Head of Data Science, Business Ecosystem, you'll be based onsite in the Grab One North Singapore office. This is a Principal Data Scientist and senior individual contributor role in the Search and Personalization team, focusing on user understanding platformization and generative recommendation.

You will provide technical leadership in building shared user understanding capabilities that power personalized experiences across Grab, including search, recommendation, chatbot, notification, and other user-facing applications. You will work across Data Science, Engineering, and Product to shape the long-term technical direction, turn latest artificial intelligence research into practical solutions, and drive adoption of these capabilities across teams.

The Critical Tasks You Will Perform

• You will define the technical strategy and roadmap for user understanding and generative personalization, identifying foundational ML capabilities that can be shared across search, recommendation, chatbot, and other personalized experiences.
• You will architect and develop large-scale user understanding systems that capture long-term preferences, real time intent, behavioral patterns, interests, and contextual signals from heterogeneous user interactions across Grab.
• You will develop reusable user representations and foundation models that support diverse downstream tasks, including retrieval, ranking, recommendation, conversational personalization, targeting, and engagement.
• You will advance generative recommendation, exploring foundation models, generative retrieval, sequence modeling, and unified user-item representations to complement and evolve conventional retrieval and ranking architectures.
• You will drive the platformization of user understanding, turning successful modeling approaches into reusable representations, models, features, APIs, and serving systems adopted by multiple teams.
• You will establish rigorous offline and online evaluation methodologies to measure the quality, generalizability, and incremental impact of user understanding and generative models across downstream applications.
• You will partner with senior machine learning and platform engineers to design production architectures for large-scale model training, representation generation, real-time user understanding, and low-latency serving.
• You will evaluate emerging research in generative recommendation, foundation models, user modeling, generative retrieval, and representation learning, and lead promising approaches from research and prototypes into production.