Foresight

Student Welfare Group

KUNWARVIR SINGH

PLACED IN GARTNER

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1)     Why did you select this particular field? What options were available to you during placements?

As with the placement scenario of IIT KGP, there were a plethora of fields that were available to start my career in such as IT, Finance, Consulting, Data Analytics, and Data Science. Over the years during my degree, I found that my interests lie in Data Analytics and Data Science. Fourth Year onwards I took up subjects such as Regression and Time Series Modelling, Recommender Systems,  etc. to deep dive into this field. Both my internships were at firms where I dealt with real-life problems in these fields. Hence, when it came to placements, a Data Science oriented profile became the natural choice for me. When I got the job profile of Gartner (my current firm), it was almost perfectly aligned with what I had done during my degree and it was a job which would definitely give me a platform to grow and develop in the field of Data Science.

2) How did you get into Gartner? What was the selection procedure?

Pre-placement season, my preparation was heavily focused on Probability & Statistics, Guesstimates, and ML. My MTP project also was into Deep Learning which made a good foundation for having strong basics for several concepts. Gartner visited the campus on Day 2 Slot 1 of the placement season. The initial shortlist criteria for the PI rounds comprised of clearing 2 tests; an Aptitude based test (Online Test) and a second test focusing on Natural Language Processing (Written Test). Upon clearing the cutoff for PI round, there were 4 rounds of PI scheduled. The first round was based on a thorough questioning of technical skills and my past experience with ML. Subsequent rounds were a mixed bag of the classic CV grilling, solving case studies and HR questions. During the PI rounds, the one thing that was a constant throughout was that the interviewers were more interested in “how” the given problem was solved by me, rather than the actual solution itself. They wanted to gauge my thought process and understand my technique of formulating a solution from scratch.

3) Can you describe your work profile/average day for us and tell us whether it was what you expected when you sat for placements?

My job profile at Gartner is in the Client Retention and Client Base Expansion Vertical. A regular day at work for me involves the utilization of ML and strong data analytics skills to generate insights about the varying trends of our products and services. Automation of work processes is also another aspect of my daily routine.

4) How would you describe the work culture in your company? How is the work-life balance?

The work culture of my firm is great. The people I work with are helpful, respectful and open to communication. We have flexible working hours and get to interact with senior stakeholders on a very regular basis.

5) What are your long-term goals? How did working in Gartner help you achieve them?

Currently, I am more oriented towards having a great learning experience at Gartner to mature my skills in Machine Learning and Data Science. With time, I would like to establish myself in this field and grow professionally and technically with the firm.

6)  Any specific advice you want to give for junta sitting for placements this year and for juniors who aspire to be in Gartner?

Firstly, get your resumes up to date with all prior analytics and ML experience from courses, internships or projects. Work towards having a basic understanding of Statistics and ML. For Probability & Statistics I referred to the NPTEL course and ‘Hines’ book. For ML I studied from ‘Andrew Ng course on Coursera’ and ‘Data Analytics course offered by the CS dept’ apart from my dept coursework. On an ending note, I would like to say: “The placements season can be pretty much random. Be focused and confident. Keep your basic concepts cleared, and have a calm mind during the interviews.”

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