Market Analyst (Data)

Get to know our Team:
The team dubbed the “cool kids” is unconventional, exciting, and mysterious. We embrace a culture of teamwork, empowerment, creativity, diversity, humour, and diligence. We help shape strategic and operational decisions in the company by providing hard-to-obtain insights into the fields of transport, food, payments, and platform services. Our value proposition comes from the many offbeat techniques used to complement traditional research and analysis. We are trusted and given the freedom to explore and innovate.

Get to know the Role:

The Market Analyst (Data) is someone who is:

• Passionate about solving complex and ambiguous problems.

• Intellectually curious - never being satisfied with what the data is saying, constantly striving to derive additional insights.

• Comfortable with failing and possess the drive to keep trying.

• Critical in thinking and with a sharp eye for detail.

• Energetic and thrives in a dynamic and fast-paced working environment.

• Strong in data query/wrangling using SQL and Python/R/Scala, and data visualisation using tools like Tableau.

The day-to-day activities:

• Use quantitative tools to provide uncover strategic and operational insights on various business verticals, regional markets, competitors, and any aspect of the external environment to help shape key stakeholders' perspectives.

• Explore, analyze and aggregate data sets to provide actionable information. Create intuitive visualizations to convey these results to key stakeholders. 

• Work closely with the Market Analysts in the team to integrate quantitative with qualitative insights.

• Explore new data sources and types, and develop new capabilities to further the team’s ability to provide valuable insights into the markets of interest.

The Market Analyst (Data) is not characterized by the following:

• Specializing in ETL, pipelining and other data infrastructure tasks. Though it is common to build and maintain some pipelines, we have a dedicated Data Engineering team for this.

• Building, training and deploying machine learning models to production in support of our App. Most production models related to our App are managed by our Engineering and Data Science teams.

• Taking an academic approach to Grab’s data. That is handled by our core Data Science team.

• Responding to ad-hoc data requests from business teams. Most of the operational dashboards are maintained by our Data Analytics team.