Infosys Data Scientist interviews reported here combine a Python-focused technical screen, practical data handling, ML discussion, coding fundamentals, and project or team-fit questions.
$97K
Avg. Base Comp
$106K
Avg. Total Comp
2-3 rounds
Typical Rounds
2-3 weeks
Process Length
The reported Infosys Data Scientist process spans practical programming, applied machine learning, and how you explain work in a delivery setting. One candidate described a roughly hour-long opening screen centered on Python fundamentals—mutability, exceptions, generators, decorators, small coding exercises, pandas file handling, and database connectivity. Another candidate encountered an online assessment with SQL, Python using pandas and NumPy, ML questions, and a missing-data case before later technical discussion.
Prepare for both Python fluency and applied ML judgment. Candidates report questions on feature engineering, evaluation metrics, hyperparameter tuning, and handling class imbalance with logistic regression, alongside a medium coding round covering BFS/DFS, tree traversal, 1D dynamic programming, arrays, strings, and linked lists. Be ready to describe a past ML project clearly: what you built, why the approach fit the data, and the tradeoffs you made.
Project discussion may also cover coding standards, code review, Agile practices, and the path to production. One report says the hiring-manager conversation probed domain background, tools, and experience level, particularly manufacturing exposure. Keep a concise account of your relevant domain work and day-to-day collaboration. The available reports are limited, so emphasis can vary by team and seniority.
Synthesized from 2 candidate 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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Topics based on recent interview experiences.
Featured question at Infosys
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Top Three Salaries | |
| Merge Sorted Lists | |
| Top 3 Users | |
| Find the Missing Number | |
| Retailer Data Warehouse | |
| Bagging vs Boosting | |
| Booking Regression | |
| Size of Joins | |
| P-value to a Layman | |
| Normalize Grades | |
| Covariance vs Correlation | |
| Find Duplicate Numbers in a List | |
| Hurdles In Data Projects | |
| Fine-Tuning VS RAG | |
| Digitizing Student Test Scores | |
| RAG Strict Source Control | |
| Classification and Regression | |
| Concurrent LLM Serving | |
| Swap Variables | |
| Ticket Agent Analysis | |
| Causal Attention Debugging | |
| RAG Hallucinations | |
| Addressing Data Quality Issues | |
| String Palindromes | |
| Merchant Dashboard Design | |
| Safe Deployments | |
| Pipeline Transformation Failures | |
| Text Editor With OOP | |
| Check Matching Parentheses |
Synthesized from candidate reports. Individual experiences may vary.
Candidates report either an initial online assessment with SQL, pandas/NumPy, ML questions, and a missing-data case study, or a nearly hour-long Python-heavy technical screen. Expect to explain concepts precisely and work through small coding or data-handling tasks.
One candidate reported discussion of an ML project, feature engineering, metrics, tuning, and imbalanced logistic regression, followed by medium-difficulty coding on graphs, trees, 1D DP, arrays, strings, and linked lists. Coverage may vary by interviewer.
Candidates report questions about project ownership, coding standards, code review, Agile work, production process, tools, and experience. A hiring-manager conversation may also probe whether your prior domain background fits the team’s needs.
