Goldman Sachs Analyst Interview Questions, Process, and Tips

Round: Resume Shortlist
Experience: They shortlist around 300 resume from whole batch for next round.
Tips: Basically add your projects related to Machine Learning and coursework related to Math. GS is one of the companies which reviews resume carefully (At least I think so :P), reason being me selected even after a low CPI. Around 250 to 300 students were shortlisted from resume for second round.
GS is one of the rarest firms that will go through your resume thoroughly. Most important part is that GS is a firm you want to go for if you have a modest CPI and are genuinely interested in global markets.
They hire for Strats and Quants division. So highlight projects related to Natural Language Processing, Artificial Intelligence and Machine Learning Techniques. Mathematical coursework and internship in related field could be elaborated too. Even students with no internship experience whatsoever were selected due to their background in Machine Learning related coursework.
I have a little or no idea about programming division as it revolves around networking and related projects.

Round: Test
Experience: They started hosting test online on hackerrank. It includes three sections with equal weightage to each of them. You have 45 minutes for individual sections. There are three type of questions asked - MCQ (+10, -3), Fill in the blanks (Numbers upto 2 decimal places; +10, -0) and 2 coding questions in programming section (+20, -0).
Programming - 2 coding questions. Questions about networking, cache and DSA (postfix and prefix expression from stack related algorithms).
Quant - Cattle grazing the circular field tied from a pole at the corner of a rectangular shade with a rope. Some about practical probability problem of die throws. Other about matrices and determinant. Theoretical questions in probability covers a major portion in form of expected value, correlation and covariance calculations. This section had some integer type questions.
Data Sciences - MLT theory should be a stronghold. Particularly confusing theoretical questions. Some of them had 4 statements and then 4 options asking correct statements so you have to be good at multiple topics. Typical question include possible solution to account for missing input data and regularization among others.
Tips: You have to be good at any two of the sections. Quant is quite competitive as the questions are easier and CSE guys can attempt these too from mathematical coursework on prob and stats. Typically whole batch is rooting for this profile because of lesser chance in CS related profile.
A little brushing up old DS notes won't hurt anyone. Specially tree, graphs (algorithms like Bellman Ford, Djikstra and Prim's algo), complexity of standard algorithms and postfix, prefix expression derivation will help a long way. Coding questions demand efficient solution, otherwise you won't be able to clear all test cases because of TLE.
For quant, Prob Stats and Linear Algebra course review is highly recommended. A little aptitude (CAT related although a lot higher standard than that) will help too.
In data sciences, ML lectures will help a lot as most part is theoretical which can be solved if you have your concepts strong. Gaussian distribution, Regularizer, PCA are some of the topics which were asked about.
Duration: 2 hours 15 minutes

Round: Technical Interview
Experience: At first I couldn

Interview preparation tips for other job seekers - do prepare well for finance questions
do a course on finance if possible

Interview preparation tips for other job seekers - Study DSA