Grab Data Scientist Interview Guide

Three candidates describe Grab's Data Scientist interview, and the overlap is useful. Each reports a technical screen early in the process. One describes a Codility test with eleven multiple-choice questions plus long SQL and Python problems. Another describes ten machine learning multiple-choice questions with SQL and R. A third calls the first coding round medium difficulty. The Codility candidate says the questions were not conceptually hard but long and wordy, so reading carefully and pacing yourself matters.

After the screen, the conversations center on your past work and on business thinking. One candidate was given an e-commerce feature case and asked which metrics would show success. Another got an open business problem from a panel of three data scientists. A/B testing, especially sample size estimation, came up across multiple rounds for one candidate, so be ready to explain test design and power.

Behavioral questions also appear. One candidate was asked to describe a conflict with a coworker, and another was asked why they wanted Grab. Prepare a concrete project story that covers the business impact, not only the model.

All three candidates report no offer, and two mention weak communication or long gaps after interviews. One also found a technical interviewer hostile and the role scope unclear. Ask early what the role covers, and confirm next steps with the recruiter.