Infosys Data Scientist interview typically runs 2-3 rounds: online assessment, technical interview, hiring manager round. Timeline is usually a few weeks, with a direct, profile-specific process.
$94K
Avg. Base Comp
$163K
Avg. Total Comp
3-4
Typical Rounds
2-3 weeks
Process Length
We’ve seen Infosys lean hard on practical, day-to-day competence rather than flashy theory. Multiple candidates reported that the technical side starts with very concrete Python and pandas usage — mutable vs. immutable types, exception handling, generators, decorators, loading files, cleaning nulls, and even tiny coding exercises like pattern printing. The common thread is that interviewers keep pressing for actual logic or examples, not broad explanations. That lines up with the interview questions we saw too: basics like covariance vs. correlation, choosing k, and classification vs. regression suggest they want candidates who can move comfortably across fundamentals without overcomplicating them.
A recurring theme is that Infosys also cares a lot about how you work inside a client or delivery environment. Candidates described questions about code standards, code review, Agile, production readiness, and even day-to-day workload, which tells us they’re checking whether you can operate in a structured, process-driven setting. We also noticed a strong emphasis on domain fit: one candidate said the hiring manager kept circling back to manufacturing experience, while another heard that experienced profiles may be probed on SQL depth, deployment, monitoring, and preprocessing. In other words, this is not a place where a generic data science story goes far — our candidates report doing best when they can connect their project work to a real business context and speak clearly about the tradeoffs they made.
Synthetized from 2 candidates reports by our editorial team.
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Real interview reports from people who went through the Infosys process.
The first round was a long, fairly direct technical screen that lasted about 58 minutes, and it felt very Python-heavy. They started with the usual tell-me-about-yourself, then moved quickly into core Python concepts like mutable versus immutable datatypes, exception handling, generators, and decorators. A few questions were written as small coding tasks rather than theory, like counting the characters in my name and printing a 1,0,1,0 pattern. After that, the interviewer shifted into pandas and basic data handling, asking how to load CSV and Excel files and how to remove empty values from a file. The last part covered databases and how to connect to one from Python, including which module to use and the syntax. It was pretty straightforward if you’ve practiced the basics, but there was no room to be vague because they kept asking for examples or actual code logic.
The second round was much shorter, around 26 minutes, and was more about my project experience and how I work in a team. They asked whether my project was a product, how I got into the project and what my journey had been like, what coding standards I followed, and what process I used before moving something to production. There were also questions about code review, Agile methodology, and even how many calls I handle in a typical day, which made it feel like they were checking day-to-day working style as much as technical depth. I also heard that for experienced candidates the process can stretch to 2–3 rounds, with SQL topics like joins, window functions, and CTEs, plus Python/ML areas such as error handling, preprocessing, deployment, and monitoring, along with a few behavioral questions. I ended up accepting the offer, and my main takeaway is to be solid on Python fundamentals, pandas basics, and be ready to explain your project work clearly and practically.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Infosys
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Retailer Data Warehouse | |
| Bagging vs Boosting | |
| P-value to a Layman | |
| Covariance vs Correlation | |
| Hurdles In Data Projects | |
| Classification and Regression | |
| Why Do You Want to Work With Us | |
| Testing Constraints | |
| Your Strengths and Weaknesses | |
| ReLu vs Tanh | |
| Choosing k | |
| Empty Neighborhoods | |
| Top Three Salaries | |
| Employee Salaries | |
| Rolling Bank Transactions | |
| Customer Orders | |
| Comments Histogram | |
| Closest SAT Scores | |
| Merge Sorted Lists | |
| Subscription Overlap | |
| Prime to N | |
| Upsell Transactions | |
| Experiment Validity | |
| Manager Team Sizes | |
| Monthly Customer Report | |
| First Touch Attribution | |
| Cumulative Distribution | |
| First to Six | |
| Button AB Test |
Synthesized from candidate reports. Individual experiences may vary.
Candidates may start with an online assessment covering SQL queries, Python coding with Pandas and NumPy, basic ML multiple-choice questions, and a case study on handling missing data. The assessment is broad and tests applied data science fundamentals across coding, analytics, and machine learning.
This round focuses on core technical depth, especially Python fundamentals, pandas, SQL, and machine learning. Interviewers ask about past projects, feature engineering, model evaluation, hyperparameter tuning, and practical problem-solving such as imbalanced classification or small coding tasks.
Some candidates also face a separate coding round with medium-level DSA questions. Topics mentioned include BFS/DFS graph traversal, tree traversal, 1D dynamic programming, arrays, strings, and linked lists.
The hiring manager round is more focused on project experience, team fit, and day-to-day working style. Candidates are asked about their role on past projects, coding standards, production readiness, Agile practices, and sometimes domain-specific background or experience.