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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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.
