Principal Data Scientist - User Understanding Platformisation

About Grab

Grab stands as Southeast Asia's premier superapp, offering a wide spectrum of services from food delivery to financial management and urban mobility. Our commitment is to economically empower the region through technology and AI, manifesting our values of heart, hunger, honour, and humility.

Role Overview

You will become a key member of the Search and Personalization team, collaborating with engineers and data scientists to innovate intelligent systems aiding millions across Southeast Asia in accessing food, groceries, mobility, and other services. The role centers in our Singapore One North office and entails senior-level responsibilities as a Principal Data Scientist focusing on developing platform-wide user understanding and generative recommendation technologies.

Key Responsibilities

• Formulate and steer the technical strategy and roadmap for user understanding and generative personalization, ensuring foundational machine learning capabilities are shared across personalized applications like search, chatbots, and recommendations.
• Design and maintain expansive user understanding infrastructure that interprets long-term preferences, real-time intents, behavioral patterns, and contextual signals from varied user interactions within Grab.
• Create reusable user representations and foundation models to support multiple use cases such as retrieval, ranking, recommendations, conversational personalization, and user engagement.
• Innovate generative recommendation techniques utilizing foundation models, generative retrieval, sequential modeling, and unified user-item embeddings to enhance or replace traditional architectures.
• Lead the platformization effort by converting successful models into APIs, features, and services that are widely adopted internally.
• Develop rigorous methodologies for offline and online assessment of user understanding and generative models, measuring their quality, generalizability, and impact across use cases.
• Collaborate with senior engineers to architect production systems facilitating large-scale training, real-time representation generation, and low-latency model serving.
• Survey emerging academic and industrial advances in generative recommendation, foundation models, and representation learning, transitioning promising research into scalable production solutions.

Candidate Profile

• Possesses a Master's degree in Computer Science, Machine Learning, AI, Operations Research, or equivalent practical experience in large-scale ML system development.
• Brings at least seven years of industry experience in designing and deploying production deep learning systems focused on recommendation, search, user modeling, or natural language processing.
• Demonstrates expertise and leadership in user modeling, information retrieval, recommendation systems, representation learning, or sequence modeling, especially in processing large behavioral datasets with transferable representations.
• Experienced with modern deep learning architectures (e.g., Transformers, sequence-based models) and proficient in frameworks like PyTorch and TensorFlow.
• Familiar with large-scale ML system design involving distributed training, embedding computations, real-time user modeling, inference latency optimization, and model lifecycle monitoring.
• Skilled in Python programming and large data processing frameworks such as Spark or Scala.
• Experienced in designing experimentation frameworks including offline evaluation, A/B testing, and incremental impact analysis of shared ML components across applications.
• Capable of bridging cutting-edge research with practical production deployments, particularly for generative recommendation and foundation models, and discerning when new modeling approaches offer significant advantages.
• Adept in defining technical strategies in uncertain problem domains and influencing cross-functional teams including engineers, data scientists, product owners, and senior leadership.

Additional Information

Benefits

• Comprehensive medical and term life insurance coverage.
• Customizable benefits packages through flexible options like GrabFlex.
• Special leaves including parental, birthday, and volunteering leave.
• Support programs addressing mental and emotional well-being.
• Flexible working arrangements supporting personal commitments.

Diversity and Inclusion

Grab prioritizes an inclusive and equitable workplace, promoting diversity at every level and offering equal opportunity to all candidates regardless of background or personal attributes.