Capgemini Data Scientist candidates describe an online assessment or screen, one or two technical interviews and an HR conversation, with heavy focus on project walkthroughs, ML fundamentals, SQL and Python.
$118K
Avg. Base Comp
$135K
Avg. Total Comp
2-5 rounds
Typical Rounds
Not reported
Process Length
Candidates describe the Capgemini Data Scientist interview as applied rather than theoretical. The most repeated theme is project depth: several candidates say interviewers questioned them on feature engineering, metric choices, models used and deployment, and one says the questions stayed grounded in their resume, especially GenAI projects. Recent candidates also report GenAI questions, such as RAG architecture, handling hallucinations, and monitoring or drift for deployed models.
Fundamentals come up often. Reported questions include imbalanced data, overfitting, outliers, regression and classification metrics, bagging versus boosting, PCA and the bias-variance tradeoff. On the coding side, candidates mention Pandas, NumPy, SQL joins, Python lists, dictionaries and tuples, OOP and git basics. One candidate reports an online coding test with array and matrix problems, and another was asked what a data warehouse is.
The format varies. Some candidates describe an aptitude, English or gamified online assessment, while others describe a screening call, one or two technical rounds and an HR or manager conversation. Two candidates prepared something in advance: a shared case study in one process and an article presentation in another. One candidate had all rounds in a single day. Outcomes are mixed, including offers, rejections and a declined offer. One rejected candidate felt the expected depth on feature engineering workflows and evaluation metrics was higher than anticipated. Prepare concrete project stories that explain why you chose each tool and how you handled imbalance, outliers and overfitting. Revisit Python and SQL basics as well as ML.
Synthesized from 12 candidate reports by our editorial team.
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Real interview reports from people who went through the Capgemini process.
The process started with an online assessment that included a mix of data structures and algorithms questions, problem-solving tasks, SQL, and a NumPy question—none of it particularly difficult if you came prepared. The subsequent rounds were split into L1 and L2 interviews. The L1 round covered basics like Pandas, data cleaning, RAG architecture at a foundational level, and a deep dive into the projects I'd worked on. L2 followed a similar pattern, starting with an introduction, then moving into questions about my previous projects and experience, followed by coding challenges and specific technical questions—things like explaining the random forest algorithm and writing SQL queries. The interviewers were genuinely interested in my project work and seemed to evaluate me based on my resume and stated experience. One of my interviews even started on time and felt streamlined, which I appreciated. The technical questions were straightforward overall: Python fundamentals, AI/ML concepts, and project-specific problem-solving. I made it through both L1 and L2, but in the end I was told that my years of experience didn't match what the job description was looking for, and I didn't receive an offer.
Prep tip from this candidate
Prepare concrete examples from your previous projects and be ready to discuss them in detail—that's what interviewers focused on most. Also brush up on Pandas, SQL query writing, and basic ML algorithms like random forest, as these came up consistently across rounds.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Capgemini
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Top Three Salaries | |
| SELECTive Wine Connoisseur | |
| Largest Salary by Department | |
| Top 3 Users | |
| Size of Joins | |
| P-value to a Layman | |
| Google Maps Improvement | |
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| Transformer Encoder Layer | |
| Digitizing Student Test Scores | |
| RAG Strict Source Control | |
| Count Transactions | |
| Late Deliveries | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Swap Variables | |
| Offer Matching API Design | |
| Data Preparation for Imbalanced Data | |
| Model Product Performance Degradation | |
| Addressing Data Quality Issues | |
| Merchant Dashboard Design | |
| Scalable Data Pipelines | |
| Testing Constraints | |
| Client Solution Pushback | |
| Relational Migration | |
| Why Do You Want to Work With Us | |
| Alternative Vendor Tradeoff | |
| Your Strengths and Weaknesses | |
| Data Cleaning Experiences |
Synthesized from candidate reports. Individual experiences may vary.
Several candidates report an online assessment or screening step before the interviews. Reports mention English, logical, quantitative and verbal aptitude, IT fundamentals, data science and coding questions, and in one case a gamified section with Sudoku and memory tasks. Not every candidate describes this step, so it may depend on the profile or the hiring cycle.
Candidates typically report an early conversation covering self-introduction, current responsibilities and general fit with the role and company culture. Have a concise summary of your background and recent projects ready.
Candidates report one or two technical rounds, ranging from about 40 minutes to 1.5 hours. Reported topics include feature engineering, metrics, imbalanced data, overfitting, Pandas, SQL joins, PCA and RAG or LLM work. Interviewers are generally described as friendly and focused on how candidates applied concepts in real projects.
Two candidates mention preparing something in advance: one discussed a pre-shared case study with a technical specialist, and another presented an article the company sent. This does not appear in most reports, so treat it as possible rather than standard. Be ready to explain your choices clearly and defend them.
Candidates commonly describe a final HR or manager discussion covering communication, teamwork and motivation, sometimes combined with a technical conversation. Outcomes reported include offers, rejections and a declined offer.
