Grab’s Data Science Department works on some of the most challenging and fascinating problems in transport, economics, logistics, and the space around. We apply machine learning, simulation, forecasting, scheduling, optimization, and many other advanced techniques on our huge datasets to push our business metrics to their bounds, directly and indirectly. We foster a culture where we enjoy raising the bar constantly for ourselves and others, and that strongly supports the freedom to explore and innovate.

 

Get to know the role:

  • Design and development of robust algorithms and techniques using computer vision, text analytics, image analytics and machine learning to solve business problems
  • Integrate, simulate and test impact of algorithms and features on the overall system
  • Design and build machine learning and optimisation algorithms efficiently
  • Develop and execute necessary analyses, simulations or A/B tests to validate models and identify improvement opportunities
  • Store, retrieve and visualise results in a manner that facilitates required analyses
  • Drive product improvements and roll-out of new features
  • Effectively conceptualize analyses to business/product stakeholders

The Must Haves:

  • Ph.D. or Master’s in Computer Science, Electrical/Computer Engineering, Industrial & Systems Engineering, Operations Research, Mathematics/Statistics, or related technical disciplines
  • Minimum 2 years of relevant experience in one or more of the following:
    • Recommender Systems
    • Natural Language Processing
    • Computer Vision
    • Speech recognition
    • 3D maps
  • Proficient in statistical programming in languages such as Python and R; and strong working knowledge in RDBMS such as PostgresQL or MySQL
  • Excellent software development capabilities, preferably in C++, Java or Python; knowledge of GoLang would be an advantage
  • Self-motivated and independent learner who is willing to share knowledge with the team
  • Efficient and detail oriented time manager who thrives in a dynamic and fast-paced working environment

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