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.
Be part of the team that models and develops mid-to-high fidelity simulations of Grab’s transportation systems, modelling the movement and behaviour of agents in order to provide critical insights to improve our product offerings. We are looking for candidates who are excited about harnessing simulation techniques to solve business problems, as well as incorporating machine learning and optimisation methods for modelling of large-scale systems.
Get to know the Role:
- Conceptualise simulation framework and architecture to address identified problems
- Develop mathematical models and code detailed simulations to dynamically model Grab’s driver and passenger movements, behaviour and interactions
- Deep dive into data to conduct advanced statistical analyses and modelling
- Incorporate machine learning and optimisation algorithms and simulate their impact on the overall system
- Effectively conceptualize analyses to business/product stakeholders
- Drive product improvements and confidence before the roll-out of new features
The day-to-day activities:
- Conduct impact analysis, sensitivity analysis and simulation-based optimisation
- Develop and execute necessary tests and analyses to validate simulation models, and perform detailed analysis to flag out vulnerabilities and improvement opportunities
- Visualise simulation results in a manner that facilitates the required analyses
The must haves:
- Minimum 3 years of post-doc experience or 6 years of relevant experience in developing large-scale complex simulation models, e.g. in transportation domain
- Ph.D. or Master’s in Computer Science, Electrical/Computer Engineering, Industrial & Systems Engineering, Operations Research, Mathematics/Statistics, or related technical disciplines
- Good knowledge in at least one of the following areas: Agent Based Modelling, Discrete Event Simulation, Decision Modelling, Transport Simulation; as well as knowledge in ML and OR techniques
- Strong software development capabilities, preferably in Python and Java; proficient in statistical programming in languages such as Python and R; and working knowledge in RDBMS such as PostgresQL or MySQL
- 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 fast-paced working environment
