Senior 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, Shuttle and on-demand bus routing and scheduling, Multi-modal transport, Geospatial data mining, etc.

Our Optimisation 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.

This position reports into the data science department

Get to know the Role:

• Identify business-wide problems and be responsible for guiding the project team in solving problems using a combination of Big data and Operations Research techniques

• Manage multiple concurrent projects and drive them to successful completion
• Communicate problem formulation, solution, analyses and insights to team members and stakeholders

• 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

The must haves:

• Ph.D. in Mathematics, Operations Research, Industrial / Systems Engineering, or Computer Science, with specialization in Optimization techniques
• Minimum 4 years of relevant post-doc experience in solving large-scale complex problems, especially in transport or logistics
• Proficient in traditional and no-sql database systems; programming languages like R, Matlab, Python; and distributed computing platforms like Hadoop and Spark
• Detail-oriented and efficient time manager who thrives in a dynamic and fast-paced working environment
• Able to present complex subjects clearly and coherently to non-domain experts
• Good knowledge in stochastic modelling, assignment, queuing systems, forecasting, scheduling, simulation, optimization, etc.
• Good knowledge with exact and heuristic methods in optimization and hybridizing them for good quality solutions and computing performance
• Knowledge in Machine Learning techniques.