
Candidates for Goldman Sachs quantitative analyst roles describe a HireVue screen, then technical and behavioral interviews or a Superday mixing probability, coding, and market knowledge. Interviewers vary widely.
$122K
Avg. Base Comp
$155K
Avg. Total Comp
3-4 rounds
Typical Rounds
Not reported
Process Length
Candidates who interviewed for Goldman Sachs quantitative analyst roles describe a process that rarely tests just one skill. Most start with a HireVue or online assessment, and several report that early screens asked about the role, motivation, and current market news. One candidate said the HireVue felt like a major filter, so prepare a specific answer on why Goldman and what the job involves.
The technical content depends heavily on the interviewer. Probability and expected-value problems come up repeatedly: expected flips to get three heads in a row, a dice-game pricing puzzle, and the maximum of three samples from [0,1]. Coding is reported in Python or a language of your choice, with one candidate describing class design and another dynamic programming and recursion. Finance questions reported include Black-Scholes pros and cons, option pricing, P/E versus EBITDA multiples, and derivatives under different market scenarios.
Some candidates describe a more investor-style conversation. One was asked to pitch a stock and then defend it against pushback, and another was asked for a favorite stock and its valuation metrics. Having two or three companies you can discuss in depth looks worthwhile.
Outcomes in the quant-titled reports were mostly rejections, with a few offers. Timing reports are sparse: one candidate heard back within one to two weeks and another reported under a month, while a third waited over a month for a decision. Spend your prep time on probability setups, clean coding with explained complexity, and a short, honest account of your resume and market views.
Synthesized from 30 candidate reports by our editorial team.
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Real interview reports from people who went through the Goldman Sachs process.
The process started with a recruiter screen that felt pretty standard — they wanted to confirm my GPA, relevant coursework, and whether I had any real investing experience. I'd done some work in a student investment fund, which helped, but mostly they were just filtering out obvious mismatches. The informal part of that call was about gauging whether I could actually talk about markets coherently and whether I seemed genuinely interested, not just looking for a prestige job.
Stage two and three were where things got real. They asked me to pitch a stock I liked, and this wasn't a throwaway question. I came prepared with deep knowledge on a company I'd actually researched — business model, valuation metrics, catalysts, risks — but I realized pretty quickly that surface-level enthusiasm doesn't cut it here. They want conviction and specificity. I also got questions about what was happening in markets at the time and had to name another company I liked with similar depth. It became clear that markets literacy is the throughline of the interview.
The technical bar was lighter than I expected for a quant role, but it mattered. They grilled me on the three financial statements, how they connect mechanistically, and basic valuation concepts like P/E ratios and DCF frameworks. It wasn't about building complex models on the fly; it was about demonstrating that I understood what actually drives a company's value beneath the surface.
The part that surprised me most was the stress-test round with a senior analyst. They took my stock pitch and came at it hard: what happens if rates spike, what's your real downside case, why hasn't the market already priced this in. They weren't being hostile — they were testing whether I could defend my thesis under genuine pushback or if I'd fold. You need conviction but also intellectual honesty about your thesis's weaknesses. I also did a case study where I had to form an investment view on an industry fairly quickly, and then there were fit conversations with multiple people, including a portfolio manager. In a small team environment, they care a lot about whether you'd work well together long-term.
I ultimately declined the offer, but the process taught me something important: this role is less about quantitative prowess on a whiteboard and more about whether you can think like an investor — combining fundamental research, market intuition, and the ability to articulate and defend a thesis under pressure.
Prep tip from this candidate
Come with 1-2 stocks you can discuss in real depth — business model, valuation, catalysts, risks, and downside case. They'll stress-test your pitch relentlessly, so prepare for aggressive pushback on why the market hasn't already priced in your thesis and what breaks your investment case.
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Synthesized from candidate reports. Individual experiences may vary.
Candidates report a HireVue video interview early on, and some also describe an online coding and math assessment with probability and graph problems. HireVue prompts typically cover why Goldman, the role, and recent market or finance news, so a specific, concise answer is likely to matter.
Some candidates report a HackerRank or CoderPad-style stage, with easy-to-medium problems where you explain reasoning, complexity, and tests. One described 45-minute technical rounds on data structures and algorithm design, such as connecting points at minimum cost, without writing full code. Expect this to vary by team.
One candidate had a Zoom conversation with two team members about their grasp of the role and interest in markets. Another described a first round that moved quickly from behavioral questions into valuation, such as P/E versus EBITDA multiples. Be ready to pivot between fit and finance.
Candidates report Superdays of two to four interviews, with some interviews around 20 to 30 minutes. Reported content includes probability and statistics, Python coding, market risk, a trade or stock pitch, and behavioral or team-fit discussion. One candidate had two Superdays, the second leaning toward fit.
Reported waits differ: one candidate heard back within one to two weeks, another described a process under a month, and another waited over a month despite following up. Candidates may want to keep following up politely and not assume a fixed timeline.