Paytm Business Analyst Interview Questions & Experience Guide

Interview Preparation
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41

Company Name: Paytm

Position: Business Analyst

Application Process: I applied for the Business Analyst role at Paytm through Naukri.com and was interviewed before May 2022.

Interview Rounds:

  • Round 1 - Resume Shortlist:

    • Questions Asked: This round involved a review of my resume to assess my qualifications and experience.
    • Your Approach: I ensured my resume was concise, highlighting relevant skills and experiences without unnecessary personal details.
    • Outcome: My resume was shortlisted for the next round.

  • Round 2 - Case Study:

    • Questions Asked: I was given a case study on “New market launch strategy.”
    • Your Approach: I analyzed the case thoroughly, identified key challenges, and proposed a structured strategy focusing on market research, target audience, and execution plan.
    • Outcome: I successfully cleared this round.

  • Round 3 - Coding Test:

    • Questions Asked: The test included SQL questions at an intermediate level.
    • Your Approach: I practiced SQL queries beforehand and focused on optimizing my solutions during the test.
    • Outcome: I performed well and advanced further in the process.

Preparation Tips:

  • For the resume shortlist, keep it clean and relevant. Avoid adding personal details like photos, gender, or age.
  • For case studies, practice structuring your thoughts and presenting clear, actionable strategies.
  • For SQL, focus on intermediate-level queries and optimization techniques.

Conclusion:
The interview process was well-structured and tested both analytical and technical skills. I felt prepared for each round, but practicing more case studies beforehand could have made me even more confident. For future candidates, I’d recommend focusing on practical problem-solving and ensuring your technical skills are up to date.

42

Company Name: Paytm

Position: Business Analyst

Location: Noida

Application Process: I applied for the job as a Business Analyst in Noida. There were no specific eligibility criteria mentioned.

Interview Rounds:

  • Round 1 - Video Call (Technical):

    • Questions Asked:
      1. Sum of squares of the first ‘N’ natural numbers.
      2. Technical questions:
        • How to filter and aggregate data using pandas.
        • What is linear regression and its assumptions.
    • Your Approach: For the sum of squares problem, I used the mathematical formula to optimize the solution. For the pandas question, I explained the use of groupby and agg functions. For linear regression, I listed the key assumptions like linearity, independence, homoscedasticity, etc.
    • Outcome: Passed this round.

  • Round 2 - Video Call (Technical):

    • Questions Asked:
      1. What is the order of execution of SQL clauses?
      2. Finding the count of visitors that came today and the count of new visitors.
      3. Find the department-wise highest salary of the employees.
    • Your Approach: For the SQL order of execution, I explained the sequence (FROM, WHERE, GROUP BY, HAVING, SELECT, ORDER BY, LIMIT). For the visitor count, I used COUNT and DISTINCT in SQL. For the salary question, I used GROUP BY and MAX functions.
    • Outcome: Passed this round.

  • Round 3 - Video Call (Technical + Business Case Study):

    • Questions Asked:
      1. Find the salary of each employee per month.
      2. What is underfitting and overfitting in ML models?
      3. Explain the concept of Random Forest.
    • Your Approach: For the salary question, I used SQL to calculate monthly salaries. For underfitting/overfitting, I explained bias-variance tradeoff and solutions like regularization. For Random Forest, I described its ensemble nature and how it reduces overfitting.
    • Outcome: Selected for the role.

Preparation Tips:

  • Topics to Prepare: SQL, Python, Machine Learning, Statistics, Guestimates, Probability, Puzzles.
  • Resources:
    • SQL: W3Schools for theory and HackerRank for practice.
    • Machine Learning: Work on an end-to-end project to understand the workflow.
  • Additional Tips:
    • Focus on business understanding by reading case studies and solving guestimates.
    • Mention your GitHub profile and ML projects in your resume.

Conclusion:
Overall, the interview process was smooth, and the interviewers were friendly. I felt well-prepared for the technical rounds, but I could have spent more time on business case studies. My advice to future candidates is to balance technical skills with business acumen and practice SQL and Python thoroughly.

43

Company Name: Paytm

Position: Business Analyst

Application Process: I applied online.

Interview Rounds:

  • Round 1 - Technical Round:

    • Questions Asked: Heavily focused on SQL and Python queries.
    • Your Approach: Practiced SQL and Python skills thoroughly to ensure I could handle the queries.
    • Outcome: Cleared the round successfully.

  • Round 2 - HR Call:

    • Questions Asked: General HR questions about my background, experience, and interest in the role.
    • Your Approach: Prepared answers about my previous experiences and how they align with the role.
    • Outcome: Cleared the round.

  • Round 3 - HR Round:

    • Questions Asked: More in-depth HR questions, including behavioral and situational queries.
    • Your Approach: Focused on being honest and showcasing my problem-solving and teamwork skills.
    • Outcome: Cleared the round.

Preparation Tips:

  • Focus on practicing SQL and Python skills as the technical round is heavily based on these.
  • Be prepared to discuss your background and experiences in detail for the HR rounds.

Conclusion:
The panel was experienced, and the interview process was smooth. Practicing SQL and Python beforehand was crucial for clearing the technical round. For future candidates, I’d recommend focusing on these technical skills and being confident in discussing your experiences during the HR rounds.

44

Company Name: Paytm

Position: Business Analyst

Location: Noida

Application Process: I applied for the job as a Business Analyst through the company’s portal. The eligibility criteria were not explicitly mentioned, but the role required strong technical and analytical skills.

Interview Rounds:

  • Round 1 - Video Call (Technical Round):

    • Questions Asked:
      1. Sum of squares of the first ‘N’ natural numbers.
      2. Technical questions:
        • How to filter and aggregate data using pandas.
        • Explain linear regression and its assumptions.
    • Your Approach: For the sum of squares problem, I derived the formula and explained it step-by-step. For the pandas question, I demonstrated filtering and aggregation with examples. For linear regression, I explained the concept and listed its key assumptions.
    • Outcome: Passed this round. The interviewer appreciated my clarity and problem-solving approach.

  • Round 2 - Video Call (Technical Round):

    • Questions Asked:
      1. What is the order of execution of SQL clauses?
      2. Find the count of visitors that came today and the count of new visitors.
      3. Find the department-wise highest salary of employees.
    • Your Approach: I explained the SQL execution order (FROM, WHERE, GROUP BY, HAVING, SELECT, ORDER BY). For the visitor count, I wrote a query with GROUP BY and COUNT. For the salary question, I used a subquery with MAX and GROUP BY.
    • Outcome: Passed this round. The interviewer was friendly and provided positive feedback.

  • Round 3 - Video Call (Technical + Case Study Round):

    • Questions Asked:
      1. Find the salary of each employee per month.
      2. What is underfitting and overfitting in ML models?
      3. Explain the concept of Random Forest.
    • Your Approach: For the salary question, I wrote a simple SQL query. For underfitting/overfitting, I explained the concepts with examples and mitigation techniques. For Random Forest, I described its working, advantages, and use cases.
    • Outcome: Passed this round. The interviewers were impressed with my technical and business understanding.

Preparation Tips:

  • Topics to Prepare: SQL, Python, Machine Learning, Statistics, Guestimates, Probability, Puzzles.
  • Resources: W3Schools for SQL theory, HackerRank for SQL practice.
  • Projects: Complete at least one end-to-end Machine Learning project.
  • Business Understanding: Read business case studies and practice guestimates.
  • Resume Tips: Mention your GitHub profile and highlight ML-related projects.

Conclusion:
Overall, the interview experience was positive. The interviewers were polite and focused on both technical and business aspects. I would advise future candidates to:

  1. Strengthen SQL and Python skills.
  2. Understand ML concepts thoroughly.
  3. Practice business case studies and guestimates.
  4. Showcase relevant projects in your resume.
    I was selected for the role, and I believe consistent preparation and clarity of concepts were key to my success.
45

Company Name: Paytm

Position: Business Analyst

Location: Noida

Application Process: I applied for the job as a Business Analyst in Noida. There were no specific eligibility criteria mentioned for the role.

Interview Rounds:

  • Round 1 - Video Call (Technical Round):

    • Questions Asked:
      1. Sum of squares of the first ‘N’ natural numbers.
      2. Technical questions:
        • How to filter and aggregate data using pandas.
        • What is linear regression and its assumptions.
    • Your Approach:
      • For the sum of squares problem, I derived the formula and implemented it efficiently.
      • For the pandas question, I explained the use of groupby and aggregation functions.
      • For linear regression, I listed the key assumptions like linearity, independence, homoscedasticity, etc.
    • Outcome: Cleared the round successfully.

  • Round 2 - Video Call (DBMS Round):

    • Questions Asked:
      1. What is the order of execution of SQL clauses?
      2. Finding the count of visitors that came today and the count of new visitors.
      3. Find the department-wise highest salary of the employees.
    • Your Approach:
      • Explained the SQL execution order (FROM, WHERE, GROUP BY, HAVING, SELECT, ORDER BY).
      • Used COUNT and DISTINCT for the visitor problem.
      • Used GROUP BY and MAX for the salary problem.
    • Outcome: Cleared the round successfully.

  • Round 3 - Video Call (Technical + Case Study Round):

    • Questions Asked:
      1. Find the salary of each employee per month.
      2. What is underfitting and overfitting in ML models?
      3. Explain the concept of Random Forest.
    • Your Approach:
      • For the salary question, I used SQL to calculate monthly salaries.
      • Explained underfitting (high bias) and overfitting (high variance) with examples.
      • Described Random Forest as an ensemble method using decision trees and its advantages.
    • Outcome: Cleared the round and got selected.

Preparation Tips:

  • Topics to Prepare: SQL, Python, Machine Learning, Statistics, Guestimates, Probability, Puzzles.
  • Time Required: 12 months.
  • Tips:
    1. Focus on SQL theory from W3Schools and practice on HackerRank.
    2. Complete at least one end-to-end Machine Learning project.
    3. Read business case studies and practice guestimates for business understanding.

Conclusion:
Overall, the interview experience was positive. The interviewers were friendly and provided clear explanations. I would recommend focusing on SQL and Machine Learning concepts, along with practical projects. Also, ensure your resume highlights relevant projects and skills, like a GitHub profile or ML projects, to stand out.

46

Company Name: Paytm

Position: Business Analyst

Location: Noida

Application Process: I applied for the job as a Business Analyst through the company’s recruitment process. The eligibility criteria were not specified, but the role required strong technical and analytical skills.

Interview Rounds:

  • Round 1 - Video Call (Technical Round):

    • Questions Asked:
      1. Sum of squares of the first ‘N’ natural numbers.
      2. Technical questions:
        • How to filter and aggregate data using pandas.
        • Explain linear regression and its assumptions.
    • Your Approach:
      • For the sum of squares problem, I derived the formula and implemented it efficiently.
      • For the pandas question, I explained the use of groupby and aggregation functions.
      • For linear regression, I discussed the assumptions like linearity, independence, homoscedasticity, and normality.
    • Outcome: Cleared the round successfully.

  • Round 2 - Video Call (Technical Round):

    • Questions Asked:
      1. Order of execution of SQL clauses.
      2. Count of visitors and new visitors for the day.
      3. Department-wise highest salary of employees.
    • Your Approach:
      • Explained the SQL execution order (FROM, WHERE, GROUP BY, HAVING, SELECT, ORDER BY).
      • Used COUNT and DISTINCT for visitor queries.
      • Applied GROUP BY and MAX for the salary question.
    • Outcome: Advanced to the next round.

  • Round 3 - Video Call (Technical + Case Study Round):

    • Questions Asked:
      1. Calculate the salary of each employee per month.
      2. Explain underfitting and overfitting in ML models.
      3. Concept of Random Forest.
    • Your Approach:
      • Used SQL to calculate monthly salaries.
      • Defined underfitting (high bias) and overfitting (high variance) with examples.
      • Explained Random Forest as an ensemble method using decision trees.
    • Outcome: Selected for the role.

Preparation Tips:

  • SQL: Practice extensively on platforms like HackerRank and learn theory from W3Schools.
  • Machine Learning: Complete at least one end-to-end ML project.
  • Business Understanding: Read case studies and practice guesstimates.
  • Resume: Highlight GitHub projects and ML-related work.

Conclusion:
The interview process was thorough but fair, with a mix of technical and business-oriented questions. Preparing well for SQL, Python, and ML concepts was crucial. I recommend focusing on practical applications and understanding business scenarios to stand out. The interviewers were supportive, which made the experience positive.