Data Scientist (Optimization)

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 Optimization team identifies and solves real-time and large-scale transportation problems using a combination of Big data and Operations Research (OR) techniques. We build, validate, test, and deploy models and algorithms using proven and experimental techniques. We are looking for scientists who are passionate about data and want to apply advanced Operations Research techniques to solve real-world problems.

Get to know the Role:

• Identify and solve business-wide problems using a combination of Big data and Operations Research (OR) techniques
• Build, validate, test, and deploy models and algorithms using proven and experimental techniques
• Define hypotheses, develop and execute necessary tests, experiments, and analyses to prove or disprove them
• Develop creative algorithms by employing OR techniques
• Translate data speak to human speak by effectively conceptualizing analysis to team members and business stakeholders

The must haves:

• Good knowledge in stochastic modelling, assignment, queuing systems, forecasting, scheduling, simulation, optimization, etc.
• Knowledge in Machine Learning techniques would be an advantage.
• Experience presenting complex subjects clearly and coherently to non-domain experts
• Experience working independently and in a team
• Ph.D. in Operations Research, Industrial/Systems Engineering, or Computer Science
• Minimum 2 years of relevant post-doc experience in solving large-scale complex problems, especially in transport or logistics
• Proficient in RDBMS such as PostgresQL or MySQL; and programming languages like R, Matlab, Python
• Self-motivated and independent learner who is willing to share knowledge with the team
• Detail-oriented and efficient time manager who thrives in a dynamic and dynamic working environment