Grab
Technology
SeniorDataScientist(SearchandRecommendations)
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optimal for mid candidates.
“Senior Data Scientist (Search and Recommendations) at Grab. Skills: Deep learning, LLM integration, Search ranking, Recommendations. Design and implement deep learning algorithms for search. Fine-tune and deploy Large Language Models (LLMs) to”
What You'll Achieve.
Improve search query understanding; Improve recommendation relevance; Validate model performance; Ensure measurable improvements to user engagement
Industry & Context.
Data insights; User behavior analysis
What They're Looking For.
Must Have
Master's in Computer Science, Operations Research, Statistics, or equivalent quantitative field, or equivalent practical experience building production ML systems at scale, At least 3 years of experience building and deploying deep learning models for search, recommendation systems, or NLP applications in production environments, Hands-on experience with Python and Scala to build data pipelines and model serving systems that process high-volume user interaction data, Proficiency in at least one deep learning framework (PyTorch, TensorFlow, or JAX) to implement neural network architectures for ranking and sequence modelling, Experience with LLM fine-tuning and deployment using frameworks such as Hugging Face Transformers or similar tools to adapt pre-trained models for search and recommendation tasks, Demonstrated experience with A testing frameworks and statistical evaluation methods to measure model impact on user behaviour metrics, Experience deploying models to production using ML serving infrastructure (such as TensorFlow Serving, TorchServe, or cloud-based ML platforms) and optimising for latency constraints
What You'll Do.
Design and implement deep learning algorithms for search
Fine-tune and deploy Large Language Models (LLMs) to
Lead offline evaluation design and online A testing
Ensure measurable improvements to user engagement before production
Partner with software engineers to scale models from
Optimize model serving and latency requirements
Translate data insights into concrete product features
Define requirements based on user behavior analysis
Monitor model performance in production
Implement retraining pipelines to maintain prediction quality
How You'll Work.
Team & Collaboration
Engineering teams; Product teams; Business teams; Software engineers; Product managers; Business operations teams
Full Job Description
About Grab and Our Workplace Grab is Southeast Asia's leading superapp. From getting your favourite meals delivered to helping you manage your finances and getting around town hassle-free, we've got your back with everything. In Grab, purpose gives us joy and habits build excellence, while harnessing the power of Technology and AI to deliver the mission of driving Southeast Asia forward by economically empowering everyone, with heart, hunger, honour, and humility. Get to Know the Team You will join our Search and Recommendations team — join a group of machine learning engineers and data scientists who build the algorithms that help millions of users discover food, groceries, and services across Southeast Asia. We work with engineering, product, and business teams to turn data into features that directly impact how users navigate our platform. Get to Know the Role You will report into the Senior Data Science Manager, and work onsite at Grab One North Singapore office. You will design and build machine learning systems that power search ranking and personalized recommendations at scale. Your work will span deep learning model development, LLM integration, and production deployment. You will measure your impact through offline evaluation metrics and online A/B test results that directly affect user engagement and business metrics. The Critical Tasks You will Perform * You will design and implement deep learning algorithms for search ranking, session-based recommendations, and multi-objective personalization systems that serve millions of users daily. * You will fine-tune and deploy Large Language Models (LLMs) to improve search query understanding and recommendation relevance. * You will lead offline evaluation design and online A/B testing to validate model performance, ensuring measurable improvements to user engagement before production release. * You will partner with software engineers to scale your models from prototype to production, including model serving opti
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