Uma Maheswara Reddy who is placed at MuSigma talks about his interview experience and other tips to prepare for placement season. Check out what he says about the placement season.
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Uma Maheswara Reddy’s MuSigma Interview Experience
- Interviewee :- Uma Maheshwara Reddy
- Branch :- CSE
- Year Of Passout :- 2024
- CGPA Cutoff :- 6
- Profile Offered :- Decision Scientist Trainee
- Job Location :- Banglore
Contact Details
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Interview Questions
Ques. Introduce yourself to the readers
Hello everyone, I’m Uma Maheswar Reddy, and I’m really glad to introduce myself as I begin my journey with Mu Sigma.
I come from a background where I’ve always been interested in technology and problem-solving, but I’m also aware that I’m still in the learning phase. My strengths right now are in Python fundamentals, basic data analysis, and understanding core concepts like loops, functions, OOP, and SQL basics. I can write clean, logical code, but I’m still improving my speed and confidence.
When it comes to DSA, I know the fundamentals — arrays, strings,queues,stacks,linked lists, lists, dictionaries, loops, basic searching and sorting, recursion, and simple problem-solving patterns. I’m not an advanced competitive programmer, and I’m still improving in topics like dynamic programming, complex graphs, and time-space optimization. But I’m consistent with practice and improving step by step.
On the machine learning side, I’ve worked on a practical project — a Traffic Sign Recognition model using CNNs. Through that project, I learned data preprocessing, augmentation, training a CNN, evaluating a model, and even real-time prediction through webcam. I also experimented with building an emotional AI chatbot, which taught me the basics of NLP workflows and model design. I’m not an ML expert yet, but I understand the pipeline and enjoy learning through hands-on projects.
Communication-wise, I’m naturally an introvert, and I’ve been actively working on improving. I practice speaking daily, and I’ve seen good progress. I’m honest about the fact that this is still a growing area for me, but I’m committed to developing strong communication skills — especially because it’s important for a Decision Scientist.
Ques. What was the difficulty level of the interview? (1- very easy, 10-very difficult)
6
Ques. What was the procedure of the placement that took place? (Shortlisting criteria/ coding or aptitude test/ number of interviews)
Shortlisting Criteria
Candidates were first shortlisted based on the basic eligibility requirements such as CGPA and branch. Everyone who met the minimum criteria was allowed to move to the next stage.
Online Test with Webcam
The first evaluation was an online aptitude test conducted under webcam supervision. It mainly included logical reasoning, quantitative aptitude, and analytical thinking questions. Continuous proctoring ensured fair evaluation.
AI Bot Round (Resume-Based + HR Questions)
Candidates who cleared the online test were required to complete the AI bot round.
In this stage:
The questions were personalized based on the resume submitted, focusing on projects, skills, and experiences.
Along with resume-based questions, the bot also asked general HR and scenario-based questions to evaluate structured thinking, communication, and confidence.
All answers were recorded through the webcam and evaluated automatically.
Personal Interview
The final step was a one-on-one interview. It included:
Basic technical questions (Python basics, simple SQL, your own projects)
HR questions (strengths, teamwork, communication, motivation to join Mu Sigma)
Practical reasoning or puzzle-style questions
The interview mainly assessed clarity of thought, attitude, and problem-solving ability.
Ques. Tell us about the written/online test. (Aptitude, Coding, Test Platform, etc)
The Mu Sigma online test was a webcam-monitored assessment focused mainly on analytical ability rather than coding. The test included sections on quantitative aptitude, logical reasoning, pattern recognition, and basic problem-solving. Questions were moderate in difficulty and checked clarity in fundamentals rather than speed. There were no heavy coding or DSA problems—only simple programming-logic questions. The platform restricted tab switching and ensured continuous proctoring. Overall, the test assessed a candidate’s ability to think logically, interpret information, and solve problems step-by-step, which aligns with the skills required for the Decision Scientist role.
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