KPMG Data Scientist candidates describe an HR conversation, a technical discussion centered on projects and fundamentals, and a manager or case-presentation stage. Reports show one offer, two rejections and one withdrawn process.
$113K
Avg. Base Comp
$137K
Avg. Total Comp
2-3 rounds
Typical Rounds
Not reported
Process Length
Across the four candidate reports, the most consistent theme is being able to explain your own project work in plain language. One candidate was asked to walk through resume projects and say what they personally did. Another was asked to describe a complex project, name the Python libraries involved and talk about how they handled failure.
The technical content varies a lot. One candidate described Python, SQL and a couple of simple math or algorithm questions, plus a question about how a machine learning background could apply to auditing. Another was asked about the assumptions behind linear regression, how to approach exploratory data analysis, and had a take-home coding assignment they found vague. A third, whose process was withdrawn, was asked only to introduce themselves and list the tools they had used for generative AI, machine learning and data analysis.
The offer came after a case presentation to three interviewers, both technical and non-technical, so practice explaining your reasoning and its business relevance to a mixed audience. HR and later conversations also touched on salary expectations and why a candidate wanted to change jobs, so have answers ready for those.
Outcomes were mixed: one offer, two rejections and one withdrawn process. Reported lengths were two or three rounds.
Synthesized from 5 candidate reports by our editorial team.
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Real interview reports from people who went through the Kpmg process.
Total 3 rounds. In the first technical interview, the team manager joined and turned off his video on Teams. He asked me to introduce myself, so I mentioned my projects and experience. He then said he wanted to understand personally who I am, and I answered the question. He also asked me to explain the tools I used for Generative AI, and to explain the tools I used for Machine Learning and data analysis. Only 3 questions were asked, and the surprise is that the position was withdrawn with no further update.
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Sourced from candidate reports and verified by our team.
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 | |
| Multicollinearity in Regression | |
| Algorithm Reliability | |
| Scalable Data Pipelines | |
| Testing Constraints | |
| Increase Search Ads | |
| Client Solution Pushback | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Google Docs Drop | |
| Late Orders | |
| Statistically Significant Test | |
| Delivery Online | |
| Branch Sales Pivot | |
| Closest SAT Scores | |
| Merge Sorted Lists | |
| Experiment Validity | |
| Size of Joins | |
| Top 3 Users |
Synthesized from candidate reports. Individual experiences may vary.
Two candidates describe an opening conversation with HR. One had a video call covering background, experience and why they wanted the role. Another covered past experience, a tell-me-about-yourself introduction, salary expectations and an overview of the interview process. Questions were described as basic.
Candidates describe a technical conversation with a technical interviewer or team manager. Reported topics include Python, SQL, simple math or algorithm questions, linear regression assumptions, exploratory data analysis, and the tools used for generative AI, machine learning and data analysis. Depth differed between accounts, so prepare both fundamentals and project detail.
One candidate's second round was with the hiring manager, who asked about a complex project, the Python libraries used and how they handled failure. Another had a final interview with a team member that covered salary expectations and why they were looking to change jobs.
The candidate who received an offer finished with a case presentation to three interviewers, both technical and non-technical. The round was described as relatively simple and focused on communication and how the response was structured. There was also time at the end for the candidate's own questions.
