KPMG Data Scientist candidates reported HR and background conversations, technical or manager interviews, and—in some paths—a substantial take-home or case presentation focused on clear business communication.
$121K
Avg. Base Comp
$137K
Avg. Total Comp
2-4 rounds
Typical Rounds
3-6 weeks
Process Length
KPMG Data Scientist interviews in these reports emphasize how you explain real project work and connect technical choices to business use. One candidate described an opening conversation on background followed by Python, SQL, and simple math or algorithm-style questions, then a case presentation to technical and non-technical interviewers. Their memorable prompt asked how machine learning could apply to auditing, so prepare a concise example that ties a model choice to a practical audit use.
Be precise about your personal contribution. Candidates were asked to walk through resume projects, including a complex project, the Python libraries used, and how they handled failure. Aim to explain the problem, your own decisions, what did not work, and how you communicated the result to stakeholders without assuming deep theory will carry the conversation.
The reported process paths differ. Another candidate described an introductory interview, an English test, manager technical discussions, a two-day anomaly-detection take-home, and a later manager interview; they felt the assignment required substantially more than a short exercise. That account also mentioned clustering and data analysis. If assigned a case, make your assumptions, data approach, model rationale, and limitations easy to follow. A separate candidate reported two rounds: HR discussion of experience and expectations, then a hiring-manager project conversation. The available reports are limited, so use these paths as preparation themes rather than a fixed sequence.
Synthesized from 3 candidate reports by our editorial team.
Had an interview recently?
Share your experience. Unlock the full guide.
Real interview reports from people who went through the Kpmg process.
I interviewed for a Data Scientist role at KPMG and went through two rounds. The first round was with HR and covered my past experience, a general “tell me about yourself” introduction, salary expectations, and a brief overview of the interview process.
The second round was with the hiring manager. That conversation focused more on my background and project experience. The manager asked me to walk through a complex project I had worked on, explain the Python libraries I used, and talk about how I handled failure. I did not receive a callback after the second round, so my outcome was no offer.
Share your own interview experience to unlock all reports, or subscribe for full access.
Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Kpmg
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Rolling Bank Transactions | |
| Longest Streak Users | |
| P-value to a Layman | |
| Fair Coin | |
| Hurdles In Data Projects | |
| Spam Classifier | |
| Testing Price Increase | |
| Assumptions of Linear Regression | |
| 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.
Candidates report an opening HR or introductory discussion covering past experience, a tell-me-about-yourself prompt, salary expectations, and sometimes an overview of the process. Prepare a brief career narrative and concrete reasons your project background fits data science work in an audit or consulting setting.
Candidates report technical conversations ranging from Python, SQL, and simple math or algorithm-style questions to a hiring-manager discussion of a complex project, Python libraries, and handling failure. Expect the depth and interviewer mix to vary; clearly distinguish your own contribution from the wider team's work.
Some candidates report a case presentation before technical and non-technical interviewers, while another reported a two-day take-home involving anomaly detection from a shared database and later manager discussion. Practice presenting analysis, model choices, and business relevance in plain language; the take-home scope may be more substantial than expected.
