Data Scientist (Analytics), OmniCommerce

About the role

Grab is looking for a Data Scientist (Analytics) to join its OmniCommerce Analytics team in Singapore. The team uses data and experimentation to drive product and business innovation, giving insights to consumers, merchants and driver-partners. The role covers deals, reservations, loyalty and CRM, turning data into decisions and working closely with product managers, data scientists, engineers and stakeholders.

Key responsibilities

• Partner with product, design and engineering teams to derive insights, support experiment design and inform product decisions
• Run A/B tests, analyse results and turn them into actionable recommendations
• Help define hypotheses and success metrics with product managers and senior analysts
• Conduct deep-dive analyses of user and merchant behaviour to find trends, patterns and root causes
• Build and maintain dashboards showing real-time product and feature performance
• Track key business metrics and flag anomalies or opportunities
• Turn stakeholder requirements into insights through reports, presentations and dashboards
• Apply AI tools to speed up analytics, validating outputs against raw data, business logic and regional context

Required profile

• At least 1 year of experience in data analytics, business intelligence or data science, ideally in a B2C or internet business with large, complex datasets
• Bachelor's or Master's degree in Statistics, Analytics, Economics, Mathematics, Engineering or another quantitative subject
• Strong communication skills and ability to explain data clearly to non-technical stakeholders
• Fast learner who takes ownership of work in a fast-paced, ambiguous environment

Required skills

• Fluent SQL and Python (or R); good-to-have: machine learning and ETL pipeline experience
• Hands-on A/B testing and hypothesis testing, with an understanding of DoE
• Data visualization with PowerBI or Tableau

What we offer

• Term life insurance and medical insurance
• Parental and birthday leave, plus volunteering leave
• A confidential assistance programme
• FlexWork arrangements such as differentiated hours