Business Analytics Specialist

Get to Know the Team

At Grab Support Regional Analytics (GSRA), we believe that delivering safe and exceptional services to our customers requires a collaborative effort. To ensure the highest quality of support in the industry, we rely on experienced and experienced data analysts to provide applicable insights that guide Grab Support Operations in making decisions that benefit our end-users. The GSRA team also humanizes big data, acts as a bridge between the technical and operational sides of the business, and uses data to align with strategic goals.

You will join a high-performing team that is deeply committed to delivering the best for our end users. You will directly report to the manager. The role will be based onsite.

Get to Know the Role

• Be the expert in using data to measure and analyze business performance across markets and lines of business.
• Explore business issues and opportunities, uncover insights, and identify targeted areas for business growth.
• Partner with management and Grab Support Operations to deep dive into core issues and use data to find actionable solutions.
• Collaborate with various expert teams to roll out effective products/services and expand Grab's universe of data for building richer insights.
• Explore new data analytics capability for data-led initiatives across Grab.

The Critical Tasks You Will Perform

• Help develop and implement data-driven analytics solutions for Grab Support and Regional COE functions, focusing on operational process optimization, enhancing performance metrics, and driving productivity improvements.
• Partner with cross-functional teams to identify data requirements and design innovative analytical solutions or insights to address complex challenges effectively.
• Conduct in-depth analyses of operational data, including business/impact analysis and text analysis, to identify areas for improvement and process optimization.
• Develop dashboards to monitor the performance of Grab Support operations, support specialists, and the business overall.
• Utilize advanced statistical and machine learning techniques, such as quasi-experimental methods, NLP & LLM, to analyze large datasets and derive meaningful insights that drive business decisions, improve support performance, and mitigate risks.
• Stay up-to-date with industry trends, emerging technologies, and best practices in analytics and data science, leveraging this knowledge to enhance the organization's data-driven capabilities.
• Collaborate with data engineering teams to ensure data quality, integrity, and accessibility, while building data pipelines and infrastructure for analytics projects.
• Communicate complex analytical findings to all stakeholders, including senior management, through concise reports, presentations, and visualizations.