Senior Data Scientist (Trust & Fraud)
Grab is hiring a Senior Data Scientist (Trust & Fraud). Level rates it ; you can apply on Grab.
Level's read on this job
- On Level's scale, this data scientist role is Builds AI ●●●●: the job is building AI systems.
- The posting mentions Claude, LangChain and LangGraph among its tools and skills.
- Level classes it as a senior role.
- Grab has 436 other open roles on Level.
AI in this role
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 Our Team
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.
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.
How we rate this
Senior Data Scientist (Trust & Fraud) at Grab rates 98 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Pay
- Typical data scientist job
- $167k
This posting doesn't list pay. Use the typical data scientist figure when pay comes up. See data scientist jobs
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- Walk me through how you've used Claude in your day-to-day work.
- What are the limits of LangChain that you've run into, and how did you work around them?
- What's a project where you used LangGraph hands-on?
- Walk me through how you've used PyTorch in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: RAG, Claude, LangChain, LangGraph, and PyTorch. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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