Data Scientist (Machine Learning)

Get to know our Team:

Grab’s Data Science Department works on some of the most challenging and fascinating problems in transport, logistics, economics, and the space around. We apply deep learning, geospatial data mining, 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.

Sample of problems the Data Science Department solve - Intelligent allocation, machine/deep learning - based predictions (all sorts!), Dynamic pricing, Supply/demand forecasting and positioning, Incentives and promotions optimization, Carpooling matching, Shuttle and on-demand bus routing and scheduling, Multi-modal transport, Geospatial data mining, etc.

The Machine Learning team at Grab provides an opportunity to apply supervised as well as unsupervised learning on large scale data. We build algorithms and models to match passenger and driver, we predict time of arrival, we predict when a driver will churn (i.e. stop driving for Grab) etc. We explore models ranging from simple linear ones to ensembles to deep learning to solve a wide range of machine learning problems in areas such as natural language processing, recommendation systems, computer vision, geo-spatial data mining, and human behavior understanding.

If you enjoy predictions and algorithms, let's have a chat!

Get to know the Role:

• Build, validate, test, and deploy machine learning models (e.g. predictive, forecasting, clustering) using proven and experimental techniques. Deploy an online learning model where applicable

• Define hypotheses, develop and execute necessary tests, experiments, and analyses to prove or disprove them

• Translate data speak to human speak by effectively conceptualizing analysis to team members and business stakeholders

The day-to-day activities:

• Develop creative algorithms by employing machine learning, and data mining techniques

• Contribute to team’s innovation and IP creation

The must haves:

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

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

• Experience with machine learning framework (scikit-learn, Spark MLlib etc)

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

• Experience in building ML models at scale, using real-time big data pipelines on platforms such as Spark/MapReduce

• Familiar with noSQL, postGIS, stream processing and distributed computing platforms

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

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