Infosys Data Scientist interviews typically combine an aptitude or online assessment with one to several technical rounds covering Python, SQL, ML fundamentals, and project walkthroughs, often closing with an HR or fit conversation.
$115K
Avg. Base Comp
$140K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Candidates describe an Infosys Data Scientist process that usually opens with an aptitude or online assessment covering basic programming, SQL, and logical reasoning before moving into technical rounds. From there, the format varies more than you might expect: one candidate faced a single conversational interview focused on resume projects and communication, while others went through multiple technical rounds plus a separate HR or managerial discussion. A recurring thread across several reports is a heavy emphasis on explaining your own projects in detail—not just what you built, but the problem statement, your approach, deployment steps, and the tradeoffs behind it. Python fundamentals (mutable vs. immutable types, generators, decorators, exception handling), pandas basics, and SQL basics show up repeatedly, alongside core ML concepts like logistic regression on imbalanced data, feature engineering, and evaluation metrics. Some candidates also report questions on generative AI topics such as RAG implementation and hallucination, suggesting interviewers are probing for applied LLM awareness alongside classic ML. A smaller number of candidates encountered basic DSA (graph traversal, sorting, time complexity), and one described a hiring-manager round focused narrowly on domain background and years of experience. One candidate's experience diverged sharply—mismatched expectations about the role's scope led to a withdrawal—so treat any single account as one data point rather than the full picture. Overall, the strongest common thread is that clear, confident communication about your own work matters as much as raw technical depth.
Synthesized from 7 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 | |
| Hurdles In Data Projects | |
| Find Duplicate Numbers in a List | |
| 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 | |
| Check Matching Parentheses | |
| Text Editor With OOP |
Synthesized from candidate reports. Individual experiences may vary.
Several candidates report an opening aptitude test or online assessment covering areas such as aptitude, basic programming, and logical reasoning. One candidate's assessment also included SQL queries, Python with pandas/NumPy, ML multiple-choice questions, and a case study on handling missing data. This stage is typically described as a gate to clear rather than a deep technical bar, though it can cover a lot of ground quickly.
Candidates describe a technical round probing Python fundamentals such as mutable vs. immutable types, generators, decorators, and exception handling, often paired with small coding tasks, pandas data handling, and SQL basics. ML questions have included logistic regression on imbalanced datasets, feature engineering, evaluation metrics, and hyperparameter tuning, with interviewers pushing for reasoning rather than definitions.
Across multiple accounts, interviewers spend significant time walking through a candidate's own past ML project—the problem statement, approach, and implementation details. Some candidates report this round also touching on generative AI concepts like RAG implementation and hallucination. One candidate described solving two live coding problems in their preferred language.
Some candidates describe a follow-up round that feels part technical, part managerial. It covers past work, deployment experience, coding standards, Agile process, and how they think about generative AI in practice. This round is not reported by every candidate.
One candidate described a final HR-style conversation focused on career goals and fit rather than technical grilling. Another had a shorter round on project experience, teamwork, and day-to-day working habits. A different candidate instead encountered a hiring-manager round that leaned heavily on domain background and years of experience, so expect this stage's tone to vary.
