TCS Research and Innovation recruits researchers from tier-1 institutes, targeting both B.Tech and M.Tech students. The shortlisting process is primarily based on candidatesâ research potential and domain expertise. During the application, we were asked to choose our preferred domain for the interview. I selected Deep Learning and AI as my primary area of interest. A total of 9 students from both B.Tech and M.Tech programs were shortlisted for the role, and I was fortunate to be one of them.
Recruitment Process Overview
- Resume Shortlisting
- Technical + Managerial Interview
- HR
Round 1: Resume Shortlisting
After my resume was shortlisted, I proceeded to the following interview rounds:
Round 2: Technical + Managerial Interview
The technical interview panel consisted of four senior-level scientists from TCS Research. The round commenced with a brief self-introduction, followed by a detailed discussion of my primary research project. I was asked to walk them through the complete pipeline, starting from dataset collection to selecting the most appropriate computational metrics for evaluating my model.
1. Technical Questions
Here are some key technical questions I was asked:
- What problem are you solving, and why do you believe it is important to address?
- Could you explain your dataset in detail? How did you handle high dimensionality in your dataset?
- What feature engineering techniques did you apply?
- How did you handle missing values?
- Why did you choose KNN imputation?
- How can outliers be detected and treated effectively?
- Explain the concepts of box plots, standardization, and normalization. Why are these techniques necessary?
- Which model did you choose and why?
- Explain how OPTUNA works in brief.
- What optimization algorithm did you use, and how does it function?
- When should one use RÂē score over accuracy, and vice versa?
Following this, I was asked to elaborate on a particular feature of one of my personal NLP projects. I had to explain the complete pipeline in detailâhighlighting each step, the choice of libraries, reasons for their selection over alternatives, and their impact on the overall time complexity.
2. Managerial Round
The managerial round immediately followed the technical interview. During this round, I was asked questions to assess my interpersonal and decision-making skills, as well as my ability to thrive in a professional setting.
Some of the questions I was asked included:
- How do you justify your suitability for this role, competing against top-tier CSE/IT students?
- Which research lab do you currently work in and under which professor?
- Why aren't you working under a professor from the CSE department?
- In the context of ML for medical applications, who do you think is better suited: someone from a non-core background like yours or someone with a biology background?
- Since machine learning was not a part of your formal coursework, how did you learn it and how credible are your sources of learning?
- Are you open to relocation, if required?
- Do you have any questions for us?
These questions gave me an opportunity to express my passion for research and how my diverse background contributed to my growth in the field of Machine Learning and AI.
Round 3: HR Round
The HR round was quite relaxed. I was asked general questions related to my background, academic journey, and CGPA.
