Senior Data Scientist (Trust - Fraud)

Grab; Selangor; On-site; MYR 180,000 - 280,000; Full time

Job summary

Grab Malaysia seeks an experienced machine learning professional to combat fraud by analyzing transactional data and deploying ML models at scale. Report to the Data Science Manager II as an IC in a full-time on-site role at our Petaling Jaya office, growing expertise in agentic systems.

You will push ML and LLM boundaries, collaborating with product and software teams to ensure seamless integration of detection systems while advancing risk strategies across the Grab ecosystem.

Qualifications

• Degree in a quantitative field (CS/Math/Physics/Engineering).
• 3+ years of Python/SQL with large-scale datasets.
• Experience building and deploying ML models with TF, PyTorch, XGBoost, or similar.

Responsibilities

• Partner with teams to architect scalable DS solutions integrating ML and agentic LLMs.
• Research latest algorithms and generative AI to counter fraud.
• Prepare and augment diverse data to build high-fidelity training datasets including synthetic data via LLMs.
• Design, train and fine-tune model architectures using GNNs, Transformers, and boosted trees.
• Lead end-to-end lifecycle from deployment to monitoring with engineering teams.
• Leverage Generative AI tools to accelerate coding, testing, and productivity.

Skills

Python

SQL

Spark

TensorFlow

PyTorch

XGBoost

LightGBM

Scikit-learn

Transformers

RAG

LangChain

LangGraph

Claude

ML Deployment

Education

Bachelor's degree in a quantitative field

Tools

LangGraph

LangChain

Claude

Job description

Jora Malaysia will close on 9th September 2026. Thank you for being with us, we are cheering you on as you continue your career journey.

We are the architects of trust. Our mission is to shield the 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 the intersection of deep learning and platform security. We don't just react; we innovate, researching the latest methods to neutralise tactics before they even surface.

Get to Know the Role

You will fight fraud by analysing transactional data, developing and deploying machine learning models, and collaborating with cross-functional teams to ensure the seamless integration of fraud detection systems. Reporting to the Data Science Manager II, you will work as an Individual Contributor in a full-time, on-site role at our office in Petaling Jaya, Malaysia. In this role, you will push the boundaries of machine learning and LLM applications at a massive scale, growing your expertise in cutting-edge agentic systems. Apply today to help keep our 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 both traditional ML and agentic LLM systems.
• You will conduct cutting-edge research to incorporate the latest advancements in algorithms and generative AI into our defense systems to actively counter emerging fraud tactics.
• You will master data orchestration by preparing, augmenting, and combining diverse data types to build high-fidelity training datasets, including using LLMs to generate synthetic data and labels.
• You will design, train, and fine-tune model architectures, utilizing a toolkit that includes Graph Neural Networks (GNNs), Transformers, fine-tuned open-source LLMs (like Qwen), and Gradient Boosted Trees.
• You will lead the end-to-end lifecycle of your 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 your daily workflows to accelerate code generation, automate testing, and enhance your overall productivity.

The Essential Skills You Will Need

• You hold a degree in Computer Science, Physics, Statistics, Mathematics, Engineering, Economics, or a 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 and process 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 the ability to choose the optimal architecture for specific problems.
• You have an understanding of 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.