Information Technology and service
12 June 2018
- Grab’s Regional Data Science 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 we 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 Data Science (Data Analytics) team analyzes data from multiple sources, extracts trends/patterns and identifies improvements. It turns data into information, information into insights and insights into business decisions.
- Develop and implement data collection, data analytics and other strategies to analyze statistical efficiency and guide decision-making
- Work with data scientists and other functions to deep dive on core issues and prioritize business and information needs
- Measure and analyze algorithm and model performance, uncover insights and/or identify targeted areas for improvements
- Design experiments and A/B tests, and operationalize them
- Monitor performance metrics to identify issues, new process or feature improvement and business growth opportunities
- Effectively conceptualize analysis to various stakeholders
- Design and implement reports and performance measurement dashboards
- A Bachelor's/Master’s degree, preferably in Analytics, Statistics, Mathematics, Economics or Engineering
- Minimum 2+ years relevant work experience in an analytics or insights related role.
- Technical expertise regarding data models, data mining and segmentation techniques.
- Strong foundation in data query/manipulation using SQL and data visualization using tools like Tableau
- Strong programming languages like R, Python, SPSS, Matlab or other tools for statistical analysis
- Strong analytical skills with the ability to collect, organize and analyze significant amount of information with attention to detail and accuracy
- Adept at queries, report writing and presenting findings
- 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
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