Grab is Southeast Asia's leading super-app that provides everyday services that matter the most to consumers. Through its open platform strategy, Grab works with partners to provide safe, accessible and affordable transport, food, package, grocery delivery, mobile payments and financial services to millions of Southeast Asians.
1. Trust that you will have a safe ride
Travel with confidence knowing that Grab’s top priority is your safety. From driver safety training and vehicle safety checks, to personal accident insurance coverage for all our drivers and passengers and government partnerships to promote safety, you know we have your back.
2. Take the transport option that fits your need
We put freedom in your hands. The most transport options, at every price point, with comfort, speed and affordability – you can have it all at the touch of a button.
3.Let us take care of you
We believe that a sustainable business is one that improves the lives of the people it touches – passengers, drivers, employees, governments and society at large.
Life at Grab is all about positive disruption – and yes, crazy days are part of that package too. Still, that’s never stopped a Grabber from having fun. In fact, it’s what keeps us motivated to shake things up further.
Life as a Grabber means succeeding in a culture of passion and innovation. We are hungry to make a difference, and recognise that good decisions often come from the heart. We are humbled by our communities, and are proud to serve them with honour. We come from all over the world, united by a common goal to make life better everyday for our users.
If you share our mission of Driving Southeast Forward, apply to be part of the team today!
Grab’s Data Science (Transport) team works on the challenging and fascinating problems surrounding Grab’s Transport verticals - ensuring our passengers and drivers enjoy a great allocation and ride experience.
A sample of problems we work on includes: intelligent allocation, machine/deep learning-based predictions, online learning, car-pooling matching, on-demand routing and scheduling, multimodal transport and geospatial data mining.
We apply machine learning, geospatial and temporal data mining, simulation, forecasting, scheduling, optimization, and many other advanced techniques on our huge datasets to push our business metrics to their bounds through improved passenger/driver allocation experience. 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.
We are looking for candidates who are excited about working on challenging problems, applying their breadth and depth of specialised knowledge to design innovative solutions, and who push boundaries in seeking to continuously improve the growing suite of Transport services for our passengers and drivers.
Get to know the role:
• Find creative ways to solve passenger-driver allocation problems through passenger-driver profiling
• Conceptualise, develop and test machine learning-based models to model Grab’s driver and passenger behaviour
• Drive product improvements and roll-out of new ML-based features
The day-to-day activities:
• Deep dive into big data to conduct advanced statistical analyses that can backup your ideas for to-be-developed features
• Design, build and productionize machine learning and optimisation algorithms efficiently
• Integrate, simulate and A/B test the impact of algorithms and features
• Store, retrieve and visualise results in a presentable manner that facilitates decision-making for rollouts
• Effectively conceptualize analyses and communicate to business/product stakeholders
The must haves:
• Ph.D. in Computer Science, Electrical/Computer Engineering, Industrial & Systems Engineering, Operations Research, Mathematics/Statistics, or related technical disciplines
• Strong fundamentals in the following:
• Deep knowledge in statistics, ML, deep learning, algorithmic foundations of optimization
• Experience with ML frameworks (scikit-learn, Spark MLlib etc)
• Experience in building ML models at scale, using real-time big data pipelines on platforms such as Spark
• Experience in developing ML models for behaviour, preference or demand modelling
• Proficient in statistical programming in languages such as Python; and strong working knowledge in RDBMS such as PostgresQL or MySQL
• Excellent software development capabilities, preferably in Java, C++ 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
Really good to have:
• Experience in working with geospatial/mobility data
• Experience in parallel programming and multithreading
• Experience in optimization and simulation.
IF YOU ARE INTERESTED, CLICK "WANT TO VISIT" TO APPLY. ONLY SHORTLISTED CANDIDATES WILL BE CONTACTED.*