Data Scientist (Economist)

Get to know our Team:

• The Economics team takes on some of the most challenging and fascinating problems in Payments, Transport, Logistics, and the space around. We apply both qualitative and quantitative techniques to ensure that we make a positive impact to our passengers, drivers and merchants. We promote a culture where we enjoy raising the bar constantly for ourselves and others, and that strongly supports the freedom to explore and innovate.

Get to know the Role:

• This role focuses on the GrabPay business, and may be required to help in cross-vertical projects. As Grab’s Payments ecosystem continues to gain traction, we are looking to deepen our understanding of our consumers and merchants to drive business growth and profitability.

The day-to-day activities:

• Develop modeling and analysis for strategic planning and performance tracking

• Develop a deep behavioral understanding of our passengers, drivers, and merchants based on econometric analysis, geo-spatial visualizations, user interviews, and cultural understanding of our relevant markets.

• Translate these understandings into actionable, creative insights that improve the user experience, including but not limited to optimising prices, incentives and promotions.

• Testing and validating the insights via rapid experimentation and deployment, and working effectively with tech and product teams to translate validated insights into scaled-up, deployed products.

The must haves:

• At least 2-3 years of work experience in Finance, Economics, Psychology, Anthropology, Sociology, Computer Science, or related fields.

• Strong foundation in data query/manipulation using SQL.

• Hands on skills using R or Python in implementation of data science techniques on vast data.

• Self-motivated, independent learner, yet effective collaborator who has a knack of presenting complex subjects clearly and coherently to non-domain experts.

The good to haves:

• Payments Experience will be a plus

• Through relevant experience or academic training, developed a balance of right-brain and left-brain competencies, spanning a keen appreciation of human behaviour (e.g. deep interests in economics, sociology, anthropology and/or psychology), to data-driven analysis (e.g. randomised control trials, econometric tools)