Interview questions for Infosys Analyst - Data Science
Hi everyone, this topic is for sharing Preparation guidelines and interview experience for Infosys Analyst - Data Science
The Analyst - Data Science at Infosys involves a multi-stage assessment and interview process, designed to evaluate both technical skills and business proficiency. Below is a summary of the process and key points from the interviews you provided:
Assessment Test Rounds:
Resume Shortlist Profiles were screened for relevant data science skills, projects, and tools. Concise resumes highlighting key projects and skills were favored.
Case Study A data analysis case for a marketing company was provided. Candidates analyzed the problem, identified key issues, and proposed data-driven solutions with a clear, logical flow.
Group Discussion Discussion on the same case study. Evaluated clarity of thought, communication, collaboration, and ability to balance speaking with listening.
Interview Rounds:
One-on-one Technical Interview Focused on core data analytics and data science concepts, discussion of past projects, and ability to explain technical topics clearly. Topics reported included regularization (L1/L2), analytics workflow, and tooling (e.g., Tableau/BI).
Alternate paths reported: Some candidates (via consultant referral or campus) experienced a single Technical Interview round focused on core DS concepts and project discussions.
Interview Preparation Tips:
Brush up on fundamental ML concepts, especially regularization techniques (L1/L2), and be ready to explain when to use each.
Practice explaining technical terms and project decisions in simple, clear language.
Prepare to present your projects concisely: problem, approach, tools, metrics, and outcomes.
Review tools you’ve used (e.g., Tableau, forecasting methods, BI workflows) and be ready to justify choices.
Practice case studies and group discussions to strengthen problem-structuring, communication, and collaboration.
Stay calm and composed during one-on-one discussions; use examples from past work to support answers.
Technical/Domain (Data Science and Analytics)
What is L1 and L2 Regularization?
When would you prefer L1 vs L2 regularization in a model, and why?
Explain your approach to analyzing data for a marketing company and deriving actionable insights from the case study.
What data analytics techniques would you apply to solve a given business problem, and how would you evaluate the results?
Projects/Experience
What projects have you worked on?
Walk me through one of your data science projects end-to-end (problem, data, approach/models, metrics, insights, and impact).
Which tools and methodologies did you use (e.g., data analysis, visualization, forecasting), and why?
How have you used Tableau or other BI tools in your projects?
How do your past projects align with the requirements of this role?
Situational/Case Study and Group Discussion
Given a marketing analytics case study, identify the key problems and propose data-driven solutions and recommendations.
In a group discussion on the case, present your approach, respond to differing viewpoints, and build consensus.
HR/Personality/Behavioral
Not explicitly reported in the provided transcripts.
Conduct notes observed from the transcripts:
Communicate clearly and structure your thoughts logically.
Balance speaking and listening during group discussions; enable others to contribute.
Use concrete examples from projects to illustrate points; stay calm and composed.
If you have attended the process from your campus, pls share your experiences here; Please follow [guidelines](https://discuss.boardinfinity.com/t/interview-transcript-guidelines/22428?u=abhay-gupta-ebaf4123)
Application Process: I applied through a recruitment consultant and was interviewed in February 2024.
Interview Rounds:
Round 1 - Technical Round:
Questions Asked:
What is L1 and L2 Regularization?
Your Approach: I explained the concepts of L1 (Lasso) and L2 (Ridge) regularization, highlighting their differences in terms of penalty terms and their impact on model coefficients. I also provided examples of when each might be preferred.
Outcome: I passed this round and moved forward in the process.
Preparation Tips:
Brush up on fundamental machine learning concepts, especially regularization techniques.
Practice explaining technical terms in simple, clear language.
Review common interview questions for data science roles.
Conclusion:
The interview was straightforward, focusing on core data science concepts. I felt confident in my answers, but I could have prepared more real-world examples to illustrate my points better. For future candidates, I recommend a strong grasp of the basics and the ability to articulate your thoughts clearly.
Application Process: Applied through the company’s career portal. The process was straightforward, and I received an email notification for the interview rounds after my application was shortlisted.
Interview Rounds:
Round 1 - Coding Round:
Questions Asked:
A Python/C++ coding question (specifics not disclosed).
A data structure problem.
Your Approach: I focused on writing clean and efficient code for both questions. For the data structure problem, I chose a suitable algorithm and explained my thought process while coding.
Outcome: Cleared the round successfully.
Round 2 - Machine Learning Basics:
Questions Asked:
Explain the difference between supervised and unsupervised learning.
Describe a machine learning project you’ve worked on.
How would you handle missing data in a dataset?
Your Approach: I provided clear definitions and examples for the theoretical questions. For the project question, I discussed a recent project, highlighting my role and the tools I used. For handling missing data, I mentioned techniques like imputation and deletion based on the context.
Outcome: Cleared the round and received positive feedback on my practical understanding of ML concepts.
Preparation Tips:
Brush up on core programming languages like Python or C++ and practice data structure problems.
Revise fundamental machine learning concepts and be ready to discuss any projects you’ve worked on.
Time management during the coding round is crucial, so practice solving problems under time constraints.
Conclusion:
Overall, the interview process was smooth and well-structured. I felt prepared for the technical rounds, but I could have practiced more coding problems to improve my speed. For future candidates, I’d recommend focusing on both coding and theoretical aspects of data science to ace the interview.
Application Process: Applied through campus placement.
Interview Rounds:
Round 1 - Technical Interview:
Questions Asked:
Basic Statistics
Basic ML and DL questions
Your Approach: Focused on explaining fundamental concepts clearly and providing practical examples where applicable.
Outcome: Cleared the round successfully.
Preparation Tips:
Prepare well with the basics of statistics, machine learning, and deep learning.
Practice explaining concepts in simple terms with examples.
Conclusion:
The interview was straightforward, focusing on foundational knowledge. Ensuring clarity in explanations and staying calm helped. Future candidates should prioritize understanding core concepts thoroughly.
Application Process: Applied via a job portal in April 2024.
Interview Rounds:
Round 1 - Technical Round:
Questions Asked:
Explain the XGBoost algorithm.
How are splits decided in XGBoost when there are 10-20 features? On which feature are they divided?
Do you have any experience with cloud platforms?
What is entropy and information gain?
What is hypothesis testing?
Explain precision and recall. In which scenarios are they used?
What is data imbalance?
What is SMOTE?
Do you have any experience working with Time Series?
Code analysis of a global variable.
Find the 5th highest salary in every department.
What are window functions?
Difference between UNION and UNION ALL.
Difference between DELETE and TRUNCATE.
Your Approach: Focused on explaining concepts clearly and providing practical examples where applicable. For SQL questions, I used logical reasoning and syntax knowledge.
Outcome: Successfully cleared the round.
Preparation Tips:
Prepare the basics thoroughly, especially for SQL, Python, and Data Science.
Be well-versed with the projects mentioned in your resume and the concepts used in them.
Conclusion:
The interview was a good learning experience. I felt confident about my answers, but I realized the importance of being precise and concise. For future candidates, I’d recommend practicing SQL queries and understanding core data science concepts deeply.
Application Process: I applied via a referral and was interviewed in July 2024.
Interview Rounds:
Round 1 - Coding Test:
Questions Asked: Basic operations on dataframe using Pandas and SQL basics.
Your Approach: I practiced common Pandas operations and SQL queries beforehand, which helped me tackle the questions confidently.
Outcome: Cleared the round successfully.
Round 2 - Technical Round:
Questions Asked:
Data preprocessing-related questions, such as the steps involved.
Questions about Random Forest and Decision Trees.
Your Approach: For the data preprocessing question, I explained the steps I had followed in my previous projects. For the Random Forest and Decision Trees questions, I discussed the differences, advantages, and use cases.
Outcome: The interview went well, and I received positive feedback.
Preparation Tips:
Brush up on Pandas and SQL basics, as they are frequently tested.
Revise key machine learning concepts like Random Forest and Decision Trees.
Be ready to discuss your past projects in detail, especially the preprocessing steps.
Conclusion:
Overall, the interview process was smooth, and the questions were aligned with the role’s requirements. Practicing coding and revisiting fundamental concepts beforehand made a big difference. For future candidates, I’d recommend focusing on hands-on coding practice and being thorough with your project explanations.
Application Process: Applied through campus placement.
Interview Rounds:
Round 1 - Technical Interview:
Questions Asked:
Q1. What projects have you worked on?
Your Approach:
I discussed my academic and personal projects, focusing on the tools and methodologies I used, such as data analysis, visualization, and forecasting. I also highlighted my experience with Tableau and business intelligence tools.
Outcome:
The interviewer seemed satisfied with my responses, and I advanced to the next stage.
Preparation Tips:
Focus on explaining your projects clearly, emphasizing the skills you applied (e.g., data analysis, visualization).
Brush up on tools like Tableau and forecasting techniques.
Be ready to discuss how your projects align with the role’s requirements.
Conclusion:
The interview was a great learning experience. I realized the importance of articulating my project work effectively. For future candidates, I’d advise practicing how to present your projects concisely and confidently.
Application Process: I applied through a newspaper ad in December 2023.
Interview Rounds:
Round 1 - Technical Round:
Questions Asked:
Questions on BERT and LSTM.
Questions on Bi-LSTM and GPT.
Your Approach: I focused on explaining the theoretical concepts behind BERT, LSTM, and Bi-LSTM, along with their practical applications. For GPT, I discussed its architecture and how it differs from other models.
Outcome: Cleared the round successfully.
Round 2 - HR Round:
Questions Asked:
Salary negotiations and bonus expectations.
Your Approach: I researched industry standards beforehand and presented a reasonable salary expectation based on my skills and experience. I also discussed my willingness to negotiate.
Outcome: The discussion went well, and I received positive feedback.
Preparation Tips:
Focus on core Machine Learning concepts, especially NLP-related topics like BERT, LSTM, and GPT.
Brush up on statistical and analytical skills, as they are often tested in technical rounds.
Practice explaining complex topics in simple terms, as clarity is key during interviews.
Conclusion:
Overall, the interview process was smooth and well-structured. The technical round tested my understanding of advanced NLP models, while the HR round was more about aligning expectations. I would advise future candidates to thoroughly prepare for both technical and HR discussions to ensure a balanced performance.
Your Approach: I discussed Kafka’s architecture, its components, and how it handles data streams, drawing from my prior knowledge.
Outcome: Cleared the round.
Preparation Tips:
Keep interviewing elsewhere. Their hiring process is broken. HRs are clueless, and the process is too long.
Conclusion:
Overall, the interview process was challenging but manageable. The technical rounds were straightforward if you have a good grasp of the required skills. However, the hiring process could be more efficient. My advice to future candidates is to stay patient and keep applying to other opportunities simultaneously.
For multi-collinearity, I explained it as a statistical phenomenon where two or more predictor variables in a regression model are highly correlated.
For decision trees, I described how they work, including concepts like entropy, information gain, and splitting criteria.
For the try-catch block, I demonstrated a simple example in Python or Java (whichever was preferred) to handle exceptions.
Outcome: [Result not specified]
Preparation Tips:
Prepare the basics of machine learning algorithms.
Have a generalized overview of the latest technologies in data science.
Conclusion:
The interview was focused on foundational concepts in data science and problem-solving. It was a good opportunity to test my understanding of machine learning and programming. Future candidates should ensure they are comfortable with both theoretical and practical aspects of data science.
Application Process: The application process involved multiple rounds, starting with a resume shortlist followed by case study, group discussion, and a one-on-one technical round.
Interview Rounds:
Round 1 - Resume Shortlist Round:
Questions Asked: No specific questions were asked in this round. The focus was on evaluating the resume for relevant skills and experience.
Your Approach: Ensured the resume was concise, highlighting key skills and projects related to data science. Avoided unnecessary personal details.
Outcome: Successfully shortlisted for the next round.
Round 2 - Case Study Round:
Questions Asked: A case study on data analysis for a marketing company was provided.
Your Approach: Analyzed the case thoroughly, identified key problems, and proposed data-driven solutions. Focused on clarity and logical flow.
Outcome: Advanced to the next round.
Round 3 - Group Discussion Round:
Questions Asked: Group discussion on the same case study from the previous round.
Your Approach: Actively participated, shared insights, and listened to others’ perspectives. Maintained a balance between speaking and allowing others to contribute.
Outcome: Progressed to the final round.
Round 4 - One-on-one Round:
Questions Asked:
Technical questions about data analytics.
Further technical questions about data analytics.
Your Approach: Answered confidently, providing clear explanations and examples from past projects. Stayed calm and composed.
Outcome: Awaiting final results.
Preparation Tips:
Be confident and well-prepared for technical discussions.
Practice case studies and group discussions to improve communication and analytical skills.
Focus on key data science concepts and tools relevant to the role.
Conclusion:
Overall, the interview process was structured and challenging. The case study and group discussion rounds tested both technical and soft skills. Being well-prepared and confident helped navigate the rounds effectively. For future candidates, I’d recommend thorough preparation and staying calm under pressure.