Get to Know Our Team
We are architects of trust. Our mission: shield Grab ecosystem from evolving fraud and safety threats by turning massive datasets into applicable intelligence. From deploying sequence-based models for payment risk to applying graph algorithms that unmask complex money laundering networks, we operate at intersection of deep learning and platform security. We innovate constantly, researching latest methods to neutralise tactics before they surface.
Get to Know the Role
You fight fraud by analysing transactional data, developing and deploying machine learning models, and collaborating with cross-functional teams to integrate fraud detection systems. Reporting to Executive Director, Data Science and Analytics, you will work as Individual Contributor in full-time on-site role at Grab KL office. You will push boundaries of machine learning and LLM applications at super-app scale, growing expertise in cutting-edge agentic systems. Apply today to help keep Grab platform safe and trustworthy!
The Critical Tasks You Will Perform
- You will partner with teams to architect scalable data science solutions that translate complex operational challenges into strategic wins, integrating traditional ML and agentic LLM systems.
- You will conduct research to incorporate advancements in algorithms and generative AI into defense systems to counter emerging fraud tactics.
- You will master data orchestration by preparing, augmenting, and combining diverse data types to build high-fidelity training datasets, using LLMs to generate synthetic data and labels.
- You will design, train, and fine-tune model architectures, utilizing Graph Neural Networks (GNNs), Transformers, fine-tuned open-source LLMs (like Qwen), and Gradient Boosted Trees.
- You will lead end-to-end lifecycle of models—from production deployment to performance monitoring—iterating alongside software and product engineering teams.
- You will leverage Generative AI tools (like coding assistants and analytical co-pilots) in daily workflows to accelerate code generation, automate testing, and enhance overall productivity.
What Essential Skills You Will Need
- You hold degree in Computer Science, Physics, Statistics, Mathematics, Engineering, Economics, or related quantitative field.
- You have at least 3 years of experience with proficiency in Python and SQL, alongside practical experience using distributed engines like Spark to wrangle large-scale datasets.
- You have hands-on experience building and deploying machine learning models using standard libraries such as TensorFlow, PyTorch, XGBoost, LightGBM, or Scikit-learn.
- You have practical experience with deep learning architectures (Transformers, RNNs, CNNs) and traditional ML (Boosted Trees), demonstrating ability to choose optimal architecture for specific problems.
- You have experience with LLM and Agentic system foundations (e.g., RAG, Transformers) and hands-on experience building or using frameworks like LangGraph, LangChain, or Claude subagents.
- You have experience engaging with AI tools and emerging technologies to enhance productivity, improve workflows, and contribute new ideas.
Life at Grab
We care about your well-being at Grab, here are some of the global benefits we offer:
- We have your back with Term Life Insurance and comprehensive Medical Insurance.
- With GrabFlex, create a benefits package that suits your needs and aspirations.
- Celebrate moments that matter in life with loved ones through Parental and Birthday leave, and give back to your communities through Love-all-Serve-all (LASA) volunteering leave
- We have a confidential Grabber Assistance Programme to guide and uplift you and your loved ones through life's challenges.
What we stand for at Grab
We are committed to building an inclusive and equitable workplace that enables diverse Grabbers to grow and perform at their best. As an equal opportunity employer, we consider all candidates fairly and equally regardless of nationality, ethnicity, religion, age, gender identity, sexual orientation, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.