Lead Machine Learning Engineer (Transport)

Get to know our Team:

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.