Candidates describe KPMG's Data Analyst interviews as short and conversational, with a screening step, a light technical check in SQL, statistics or basic Python, and fit questions with managers or partners.
$94K
Avg. Base Comp
$100K
Avg. Total Comp
2-3 rounds
Typical Rounds
2-3 weeks
Process Length
KPMG's Data Analyst interviews, as candidates describe them, are short and conversational, and they weigh motivation, communication and your own resume as heavily as technical skill. The clearest accounts describe two or three conversations: a screening step with HR or online, then time with a manager, senior auditor, partner or HR.
The technical content is light and varies by person. One candidate was asked basic statistics, including what a p-value means, along with SQL and a murder-mystery style database question that rewarded the approach more than a complex answer. Another was asked SQL questions and some basic Python and data structures. A third was asked to explain a past project that hit a major challenge, then fielded technical and scenario-based questions from two senior associates.
Behavioral questions come up repeatedly: why KPMG specifically, a conflict with a teammate, leading an event, and working under pressure. Candidates reported processes of roughly two to three weeks, and the reports include both an offer received and offers declined.
For preparation, build a concise answer for why KPMG, be ready to explain every line of your resume, and refresh SQL and basic statistics so you can reason aloud rather than recite.
Synthesized from 24 candidate reports by our editorial team.
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Topics based on recent interview experiences.
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| Question | |
|---|---|
| Rolling Bank Transactions | |
| Longest Streak Users | |
| P-value to a Layman | |
| Fair Coin | |
| Hurdles In Data Projects | |
| Spam Classifier | |
| Assumptions of Linear Regression | |
| Testing Price Increase | |
| Modifying a Billion Rows | |
| Late Deliveries | |
| Data Preparation for Imbalanced Data | |
| Algorithm Reliability | |
| Multicollinearity in Regression | |
| Scalable Data Pipelines | |
| Testing Constraints | |
| Increase Search Ads | |
| Client Solution Pushback | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Branch Sales Pivot | |
| Google Docs Drop | |
| Late Orders | |
| Statistically Significant Test | |
| Delivery Online | |
| Closest SAT Scores | |
| Experiment Validity | |
| Size of Joins | |
| Top 3 Users | |
| Raining in Seattle |
Synthesized from candidate reports. Individual experiences may vary.
Candidates describe opening with an HR conversation or an online screen. One report was an online aptitude and logical reasoning screen. Another was a screening call with HR about background, why the firm and career goals, where a clear answer on why KPMG specifically mattered.
Technical questions were basic and differed by candidate. Reports include SQL and basic Python or data structures, p-values and a database reasoning puzzle, and a walkthrough of a past project with a major challenge. Interviewers seemed more interested in how candidates reasoned than in advanced theory.
Later conversations were with a partner, hiring manager, senior auditor, or HR. One candidate had two 30-minute interviews, one with a partner and one with a hiring manager, centered on the resume and a teammate-conflict question. Another had a final HR round on motivation, cultural fit and salary expectations.
