Grab Data Analyst candidates describe a process mixing recruiter and manager conversations with SQL, statistics, and metric root-cause cases. Here is what the three reports say and how to prepare.
$73K
Avg. Base Comp
$95K
Avg. Total Comp
4 rounds
Typical Rounds
Not reported
Process Length
Across three candidate reports, the Grab Data Analyst interview centers on practical analytical reasoning. Two candidates describe a metric-drop investigation: one was asked how to explain a significant performance drop in a particular month, and another worked through a fall in customer retention and its drivers. Practice structuring these out loud: clarify the metric, segment the data, form hypotheses, then explain findings to non-technical stakeholders.
SQL appears in all three accounts, in different forms. One candidate had a SQL component alongside a case study, another had a technical round covering SQL, statistics, and root cause analysis, and a third was asked SQL questions plus a question about finding anomalies in a table. That last one required some statistical thinking, so pair query practice with basic distribution and outlier reasoning.
Statistics and machine learning came up in two reports. One candidate faced a case blending statistics with ML concepts and a question on determining A/B test sample size, where trade-offs and business constraints mattered. Another reported a conceptual statistics and ML question. One candidate also reported a Codility screen with coding and statistics questions, and another wrote Python and answered a technical design question about a data pipeline.
Two candidates reported four rounds, ending with culture fit or career-focused conversations with the hiring manager or a head. Two received offers and one did not after a roughly month-long waitlist. Because the sample is small and the formats differ, treat these as examples of what may come up, and confirm the stages with your recruiter.
Synthesized from 3 candidate reports by our editorial team.
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Featured question at Grab
In which case would you use a bagging algorithm versus a boosting algorithm
| Question | |
|---|---|
| Uber Eats Success | |
| Swipe Payment API | |
| Your Strengths and Weaknesses | |
| A/B Test Power Size | |
| Employee Salaries | |
| Experiment Validity | |
| User Experience Percentage | |
| First to Six | |
| Top 3 Users | |
| Download Facts | |
| Fair Coin | |
| Button AB Test | |
| Weighted Keys | |
| 500 Cards | |
| Third Purchase | |
| P-value to a Layman | |
| Distance Traveled | |
| Normal Distribution Sample | |
| Size of Joins | |
| Average Order Value | |
| Network Experiment Design | |
| Daily Retention Summary | |
| Raining in Seattle | |
| Maximum Profit | |
| Revenue Retention | |
| Bank Fraud Model | |
| Encoding Categorical Features | |
| Demand Metrics | |
| Uber User Journey |
Synthesized from candidate reports. Individual experiences may vary.
Two candidates report an opening HR or recruiter call covering background, motivation for the role, and in one case expectations and constraints. One candidate instead reports applying for the Malaysian team and starting with a Codility test. Expect a conversation or online screen before technical rounds.
One candidate for the Malaysian team reports a Codility test mixing coding and statistics questions as the initial screen. Others did not mention it, so it may not apply to every team or location. Brush up on basic coding and statistics fundamentals in case it appears.
Candidates report technical rounds with SQL, statistics, and root cause analysis, including a retention drop scenario. One also reports Python coding, a data pipeline design question, and an anomaly-detection SQL question. Another reports an A/B test sample size question. Formats vary, so prepare broadly across these areas.
Candidates describe hiring manager conversations focused on business thinking, including how to investigate a drop in metrics in a given month. One candidate also reports a case study testing industry understanding plus SQL. Interviewers reportedly cared more about the analytical approach than memorized syntax.
Two candidates report a final conversation that was lighter and focused on culture fit. One describes scenario-based personality questions with the hiring manager and a teammate, and another a head interview about career direction. Another candidate spoke with the hiring manager and their boss before a waitlist and rejection.
