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

The Grab Integrity team is dedicated to protecting the Grab platform from multiple types of fraud and safety incidents. Our team uses rich datasets ranging from payment risk prediction using sequence-based models to detecting money laundering with graph algorithms and ensuring platform safety. We research new methods to stay ahead of latest fraud tactics, contributing to the creation of thoughtful and secure products.

Get to Know the Role

You'll fight fraud by analyzing transactional data, developing and deploying machine learning models, and collaborating with teams to ensure seamless integration of fraud detection systems. You'll help keep our platform safe and trustworthy.

This will be a paid internship role, where you will have a dedicated mentor to guide you through the learning, building and creating value during your internship.

You will report to our Head of Data Science, FinTrust and FinID. This role will be based onsite at our office in Petaling Jaya, Selangor.

The Critical Tasks You Will Perform

• Collaborate with your team to understand and convert operational issues into data science problems, aligning efforts with our strategic goals.
• Stay updated with the latest research and advancements in the field, incorporating the latest models and techniques to address new fraud tactics.
• Handle data preparation and augmentation, using multiple data types to create comprehensive datasets for model training.
• Train and improve machine learning models, ensuring accuracy and efficiency in fraud detection through careful selection and tuning of algorithms. Types of algorithms the team work on include Graph Neural Networks, Transformer/ Sequence models, finetuned LLMs, Boosted Trees
• Deploy models into production, managing their performance, and working with data scientists, software engineers, and product managers to ensure seamless integration and ongoing improvements.

Qualifications

What Essential Skills You Will Need

• Can start from May 2026 onwards with a minimum duration of 3 months
• Study for a degree in computer science, physics, statistics, or a related quantitative field.
• Proficiency in Python, SQL, and programming skills, with familiarity with numeric libraries, containers, and modular software design
• Some experience of standard machine learning libraries such as TensorFlow, PyTorch, XGBoost, LightGBM, and Scikit-learn
• Understand and some experience using traditional ML techniques like Boosted Trees, and deep neural network architectures, like CNNs, RNNs, Transformers
• Familiarity with LLM coding tools, e.g. Cursor, Claude code, or have some experience using LLMs for improving coding efficiency

Additional Information

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.
• Balancing personal commitments and life's demands are made easier with our FlexWork arrangements such as differentiated hours

What We Stand For At Grab

We are committed to building an inclusive and equitable workplace that provides equal opportunity for Grabbers to grow and perform at their best. We consider all candidates fairly and equally regardless of nationality, ethnicity, race, religion, age, gender, family commitments, physical and mental impairments or disabilities, and other attributes that make them unique.