Get to know our Team:
Grab - the leading super app in Southeast Asia - combines transport, food delivery, logistics, financial services, and much more in a single platform. The TIS (Trust, Identity & Safety) department acts as the guardian of all users on Grab and protects Grab’s all business lines.
The TIS data science team develops various AI technologies and apply them to generate fine granular risk assessment of a user, a device and an event in real time. We extensively use machine learning (including deep learning), computer vision, natural language processing and data mining technologies on multimodality dataset of users’ whole lifecycle. With the growth of Grab’s business and huge amount of activities and transactions happening hourly on the platform, there is a huge demand for data driven technologies and a great opportunity to make real world impacts through innovations.
Get to know the role:
R&D of the state-of-the-art AI technologies like a researcher.
Hands on implement and optimize models and systems for production as a hardcore engineer.
Coach, lead and help fellow data scientists like a leader.
Communicate with business and product teams as an ambassador.
The must haves:
Ph.D. or Master’s degree in Computer Science, Electrical/Computer Engineering, or related technical disciplines.
10+ years programming experience, proficient in languages like Python or C++.
Proficient in algorithm design given various data structures including sparse matrices, sequences, trees, and graphs.
8+ years experience developing machine learning, deep learning and data mining technologies in big volume and high velocity scenarios..
Expert level knowledge and 4+ years experience in one or more of the fields: RNN, computer vision, and NLP.
Strong capability to shape the technical strategies and roadmaps.
Self-motivated, independent learner, and enjoy sharing knowledge with team members.
Really nice to haves:
Deep understanding of the fraud space with hands-on knowledge of fraud, payments and risk, especially on tech products.
Expert knowledge and real world experience in large scale imbalance machine learning, unsupervised learning, Graph neural networks.
Experience in pruning, optimizing and deploying deep learning models for edge devices.
Recent publications in top conferences or journals.
Awards in programming, math or machine learning related competitions.