Senior 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.

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

Get to know the Role:

• Build, validate and deploy machine learning models (e.g. predictive, forecasting, clustering)
• Be the technical expert in the team, and guide the team on best practices
• Deploy an online learning model where applicable
• Gain access to lot of structured data, and develop creative algorithms by mining that data
• Define hypotheses, develop and execute necessary tests, experiments, and analyses to prove or disprove them
• Contribute to team’s innovation and IP creation

The must haves:

• Ph.D. in Computer Science, Electrical / Computer Engineering or Mathematics / Statistics, with at least 4 years of experience in building ML models at scale
• Deep understanding of ML algorithms such as clustering, logistic regression, neural networks, SVM, decision trees, boosting techniques, reinforcement learning
• Experience in mentoring junior team members on technical subjects
• Proficient in statistical programming in languages like Python, R and in RDBMS such as PostgresQL or MySQL
• Working knowledge of Big Data technologies such as Spark, Hadoop
• Detail-oriented and efficient time manager in a dynamic and dynamic working environment