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#SGUNITEDJOBS LEAD DATA SCIENTIST
  • Python
  • Machine Learning
  • Excel
  • Data Mining
  • Deep Learning
Virtual Career Fair
Singapore
172 days ago

The team:

Grab - the leading super app in Southeast Asia - combines transport, food delivery, logistics, payments, and much more in a single platform. The trust, identity and safety (TIS) department acts as guardians of all users on Grab and protects Grab’s all business lines.

The TIS data science team builds AI models and systems to generate fine granular risk assessment of a user, a device and an event in real time. We extensively use computer vision, machine learning and language processing and data mining technologies on multimodality dataset of users’ whole lifecycle. With the growth of Grab’s business and tremendous amount of activities and transactions happening hourly on the platform, there is huge demand for AI technology and great opportunity for innovation.

Responsibilities

The successful applicant would be expected to work on challenging problems in computer vision and deep learning that can ultimately scale up the AI capability of Grab’s platform. This will involve generating novel ideas in different application scenarios, presenting these ideas to stakeholders and engineering real-world systems.

Requirements

  • Ph.D. or Master’s in Computer Science or related field
  • Solid research and engineering background and experience in computer vision, machine learning, and image processing/analysis algorithms.
  • 6+ years work experience in a relevant field.
  • Current knowledge of the latest development in deep learning for visual recognition.
  • Excellent written and oral communication skills also required.
  • Proficient in one or more of the following programming languages: Python, Golang, C/C++.
  • Hands on experience using modern deep learning frameworks (e.g. TensorFlow, PyTorch, MxNet).
  • Experienced in model pruning and compression is a plus
  • Hands on experience in face recognition and anti-spoofing algorithms is a plus.
  • Hands on experience in OCR in variance contexts is a plus.

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