Data Scientist (Behavior Modelling)

Get to know our Team:

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:

• Report into the Data Science Dept

• Explore and extract insights from massive dataset of geospatial, behavioural, economic, and the interaction of millions of passengers and drivers and millions of rides per day from more than 65 cities across the region

• Build, validate, test, and deploy models to predict and influence the behaviour of supply and demand with the goal of optimising system efficiency of the platform

The day-to-day activities:

• Study and modelling behavior of passengers and drivers throughout the complete business cycle (such as making a booking or accepting a trip request) and the factors influencing their behavior

• Develop creative algorithms by employing machine learning and other data science techniques

• Collaborate with other data scientists, software engineers, and business operation teams

• Opportunity to work with research groups from top universities

The must haves:

• Ph.D. graduate, or Masters (with at least 3 years of experience), in Economics, Computer Science, or Mathematics/Statistics

• Proficient in one or more of the following programming languages: Python, R, Scala.

• Experience with one or more big data processing frameworks such as Spark

• Understanding of machine learning, deep learning, data mining, algorithmic foundations of optimization

• Experience or knowledge in Behavioral Economics will be advantageous

• Self-motivated, independent learner, and willing to share knowledge with team members

• Detail-oriented and efficient time manager in a dynamic working environment