Cognizant Data Scientist candidates report a variable process that commonly combines an assessment, a project-centered technical discussion, SQL and Python questions, and an HR or managerial conversation.
$123K
Avg. Base Comp
$150K
Avg. Total Comp
2-4 rounds
Typical Rounds
Not reported
Process Length
Cognizant Data Scientist interviews reported here are practical and broad rather than centered on one advanced algorithm. Project ownership is a recurring theme: candidates describe being asked to walk through an ML or predictive-analytics project end to end, explain tools and technical choices, discuss obstacles, and connect the work to requirements or outcomes. Be ready to explain your own contribution rather than recite a high-level project summary.
SQL appears repeatedly, both in an online assessment and in technical interviews. Reported prompts include complex joins, aggregations, duplicate rows, the second-highest salary without LIMIT, unique values without DISTINCT, normalization, and key differences. Python questions range from fundamentals such as collections, recursion, and OOP to straightforward coding tasks such as removing duplicates, counting characters, or finding the second-largest number.
Candidates also report applied discussions of machine learning, clustering versus classification, feature engineering, evaluation, error analysis, data cleaning, AI and generative AI. Some accounts include aptitude or model-completion work before interviews, while others describe a shorter technical-plus-HR sequence. The evidence is thin on a single standard format, so prepare for variation while keeping project explanations, SQL reasoning, and clear communication equally sharp.
Synthesized from 5 candidate reports by our editorial team.
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Real interview reports from people who went through the Cognizant process.
The frustrating part of my Cognizant interview was that I was rejected even though I passed all three coding questions and, when I checked my answers afterward, they still appeared correct. I had applied online and heard from a recruiter two days later. The next step was a 30-minute call with the hiring manager, who focused on three coding questions rather than spending much time on general discussion.
SQL was a central part of the interview. One specific prompt asked me to return unique values without using DISTINCT, so it was important to know alternative ways to express a query instead of relying on the most obvious syntax. The broader technical areas emphasized for this role included Python, SQL, machine learning, and ETL pipelines. Questions could also cover SQL join types, including explaining the differences among three kinds of joins. Project experience was relevant as well, particularly being able to discuss projects connected to the role. The questions themselves felt more foundational than highly algorithmic, but the expectation was that I could answer accurately and show solid command of the underlying topics.
I received a rejection the following day and was not given any feedback. That made the decision difficult to interpret, especially because the submitted solutions seemed correct. My takeaway is that correct code may not be the only factor being evaluated, so I would prepare to explain my reasoning clearly while solving each problem. I would also be ready for broader questions about Python, machine learning, ETL work, and role-related projects, even if the interview appears to be centered on SQL.
Prep tip from this candidate
Practice writing SQL queries that return unique values without DISTINCT, and be able to clearly compare the main join types. Also prepare concise explanations of your Python, machine-learning, and ETL projects, since the technical discussion can extend beyond the coding prompts.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Cognizant
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Largest Salary by Department | |
| Size of Joins | |
| ATM Robbery | |
| Random Forest Explanation | |
| Sort Strings | |
| Hurdles In Data Projects | |
| Dijkstra implementation | |
| Find the Missing Element | |
| Implementing the Fibonacci Sequence in Three Different Methods | |
| Swap Variables | |
| Overfit Avoidance | |
| Search Increase | |
| String Palindromes | |
| The Pirate’s Hunt | |
| Impossibly Iterative Fibonacci | |
| Text Editor With OOP | |
| Fixed-Length Arrays: Deletion | |
| Azure Kubernetes Infrastructure | |
| Client Solution Pushback | |
| Why Do You Want to Work With Us | |
| Your Strengths and Weaknesses | |
| Top Three Salaries | |
| Rolling Bank Transactions | |
| Closest SAT Scores | |
| Merge Sorted Lists | |
| Employee Salaries | |
| Top 3 Users | |
| Experiment Validity | |
| First Touch Attribution |
Synthesized from candidate reports. Individual experiences may vary.
Some candidates report an initial online assessment with aptitude questions and SQL coding; another account describes an initial coding-and-aptitude round and model-completion work. SQL prompts may involve joins, aggregations, query efficiency, or a familiar task such as second-highest salary.
Candidates report technical conversations centered on a resume project or assigned model, including the workflow, tools, choices, challenges, and impact. Interviewers may then move between SQL, Python fundamentals, data structures, machine learning, clustering, data cleaning, and model evaluation.
Reported exercises include writing SQL queries, finding a second-largest number, counting a character in a string, and removing duplicates from a list. Candidates also describe explaining joins, keys, normalization, lists versus tuples versus sets, recursion, OOP, and ML concepts.
Several candidates report an HR, managerial, or combined discussion after technical evaluation. Questions may cover communication and attitude, motivation for Cognizant, relocation, future goals, or handling conflict; project experience can remain part of the conversation.
