Lead Data Scientist (Forecasting)

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.

Get to know the Role:

• Lead and build a team of Data Scientists

• Identify and solve forecasting problems using a combination of time series statistical modelling, machine learning (ML) and operations research (OR) techniques.
• Detect data contamination and outliers and clean the data using relevant algorithms.
• Define hypotheses, develop and execute necessary tests, experiments and analyses to prove or disprove them.
• Build, validate, test, and deploy models and algorithms using proven and experimental techniques.
• Effectively conceptualize analyses to team members and business stakeholders.
• Reports into Data Science Department

The must haves:

• Ph.D. in Mathematics, Statistics, Industrial & Systems Engineering, Economics, Decision Science or Computer Science.
• Minimum 3 years of relevant post-doc experience in solving large-scale complex problems, especially in finance forecasting, demand forecasting.
• Proficient in big data platforms such as Apache Spark; and programming languages like R, 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 fast-paced working environment.

• Experience in leading a team of data scientists, preferably in a startup or a tech company

• Good knowledge in time series analysis, artificial neural networks, forecasting, optimization etc.
• Knowledge in ML and OR techniques would be an advantage.
• Experience presenting complex subjects clearly and coherently to non-domain experts.

• Detail-oriented and efficient time manager in a dynamic and fast-paced working environment