Goldman Sachs Quant Analyst Interview Experience (Full time)
Hey everyone! This post is about my campus placement at Goldman Sachs for the role of Quantitative Analyst and some insight into the process.
About me
I was a dual degree student (Mathematics and Computing, 2025 batch) and graduated from IIT Delhi in 2025. Currently, I work at Google as an ML SWE. My past experiences include internships at Uber and Max Planck Institute.
Some backstory
I interned at Uber during the summer of 2024, but I had made up my mind to sit for the campus placements (due to personal reasons). To be eligible for campus placements, I had to reject the PPO offered by Uber.
Rejecting the PPO was a difficult decision, especially since I had no other offers at the time. The uncertainty was daunting, and I was aware of the risk of regretting turning down an opportunity from a good company like Uber. Nonetheless, I said, “jo hoga dekha jayega” and starting prepping for the upcoming placement season.
At the same time as the on-campus placement processes were going on, I received some off-campus opportunities from Google and Rubrik, and I was able to convert them successfully into a Full time offer (Google) and a 6 month intern offer (Rubrik)
So before my final interview for GS on 1st December, I was already quite calm and relaxed, thanks to the backup offers I had in hand :)
Lets jump to the process now.
Disclaimer: I am writing this blog almost 1 year later, some of the details might be outdated/not accurate
Process
There were 2 parts: an Online Assessment (for shortlisting candidates for interview) followed by the interviews itself.
Resume shortlist: As far as I remember, we had a CG cutoff criteria of 7.5+
Online Assessment
For the Quant Analyst profile, we had both coding and quant assessments in the OA. Coding questions were mathematical/logical questions only, not something that required very in-depth knowledge about data structures, but required experience with quant style mathematical questions
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The quant questions contained some problems revolving around Probability, Markov Chains, Game theory, and ofc, the famous resource: Brainstellar.
Interviews
I had interviews with 3 different panelists.
The first panel’s interviewer started the interview with this question : “Why did you reject Uber’s PPO? “. I had come prepared for answers to this question. (By the way, this was the single common question that all 3 panelists and the HR as well posed to me multiple times befor giving me the offer).
He gave me a simple problem related to finding square root of a number upto certain precision without using any library function. I wrote some code using binary search for upto the precision specified. Then we extended the problem to a more general case where the precision wasn’t specified. I gave solution by the long division method (something we all learnt in elementary school I guess?)
This was followed up with some questions related to projects in my resume, specially related to some quant challenges I had been a part of (like Alphagrep’s alpha finding challenge, and JP Morgan’s Machine Learning Quant Challenge). Also talked about a question related to Markov Chain and Game theory.
The second panelist focused on asking questions which were directly picked from Brainstellar. In addition to that, he also asked some probability distribution related questions. I don’t remember them exactly, but it revolved around having some variables a, b, c and their distributions were specified. An inequality between these variables was given, and I was asked to solve it. I drew a graph and solved the problem graphically (maybe it was something like finding the area enclosed between them)
Another coding question was asked here, which I do not remember
The third panel focused more on talking about autoregressive models and time series analysis (because I had a project on them in my resume). We talked about multiple technical definitions and meaning of “terms” in this round. We also solved problems from topics like multivariate statistics. I remember I was asked some concepts related to PCA (Principal Component Analysis). I would describe this round as one that focused more on applying the theoretical concepts we learned during our mathematics degree to practical problems that the industry solved.
Result
I received the offer. My interview processes which started at 6 AM on day 1 were wrapped up by 7 AM with an offer from my preference lists’ first company, so my campus placements were over :)
Fun fact: this was the year when GS had raised the compensation by ~50% (only for the Quant role) for fresh grads as compared to what they had offered in the previous years.
Final thoughts
A lot of the process can be the candidate driven instead of being interviewer driven. This can give you an edge in the area you choose for your ownself. It is like making your own test to solve. So try to lead the interview in a direction that makes the interviewer ask you questions that you already have prepared answers for :) I had tailored my resume in such a way, and often talked about some topics which would definitely catch the interviewers attention, and then the interviewer ended up discussing the topics I was prepared for.
I was able to perform upto my potential primarily because I was calm. Anxiety and panic makes you perform lesser than your potential. So relax, take a deep breath, and be yourself.
Always remember, campus placements are not just measuring your potential. Multiple campus dynamics play a huge role in these processes. Luck is an underrated factor. You might have done everything right, but there also exist factors beyond the scope of your control. In such unfortunate cases, it’s best to move on as quickly as possible, and make the best of what still is in your hands.
If you have any doubts or questions about the process, I am happy to help you in any way I can. Feel free to reach out on LinkedIn
