Goldman Sachs Data Scientist interview typically runs 4–5 rounds: recruiter screen, HireVue video interview, technical screen, and a Superday with 2–5 back-to-back interviews. The process spans roughly 4–8 weeks and is distinguished by deep theoretical ML and probability rigor throughout.
$120K
Avg. Base Comp
$231K
Avg. Total Comp
4-6
Typical Rounds
4-8 weeks
Process Length
Our candidates consistently report that Goldman Sachs is far more interested in the derivations behind your modeling choices than in surface-level familiarity with algorithms. Across multiple 2026 experiences, the same themes keep surfacing: loss functions, logistic regression, tree-based methods, and the tradeoffs between boosting and bagging. What stands out is the level of rigor around why a method fits a problem — one candidate specifically noted being pushed to derive the intuition behind a loss function from scratch, which is a strong signal that this team wants people who understand the math beneath the model, not just the model names.
A second pattern we've seen is that Goldman Sachs screens for composure and precision under pressure, not just raw knowledge. One candidate described navigating a probability tree and Buffon's needle problem while being expected to narrate their reasoning step by step, while another faced rapid-fire regression questions that rewarded clean, confident answers over long pauses. That combination — deep ML theory in one room, applied probability and statistics in another — tells us the Superday is deliberately designed to stress-test both dimensions. The non-obvious make-or-break is almost never a missing buzzword; it's a small conceptual slip when justifying a modeling choice or explaining an objective function under time pressure. Multiple candidates who cleared early rounds noted that a single rushed or imprecise answer in the final stage was enough to change the outcome, which makes deliberate, out-loud explanation practice especially valuable here.
Synthetized from 3 candidates reports by our editorial team.
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Real interview reports from people who went through the Goldman Sachs process.
The part that stood out most to me was the final round, which was two back-to-back 45 minute interviews. In the first one, I was asked probability questions that felt more like a math screening than a typical data science chat, including a probability tree and the Buffon’s needle problem. I had to talk through my reasoning carefully rather than just give a final answer, so it helped to stay organized and explain each step out loud. The second interview switched gears into coding plus rapid-fire statistics. That section moved quickly and covered regression concepts, so it felt less like solving one long problem and more like being tested on whether I could answer cleanly under pressure.
Earlier in the process, there was also a more standard screening style conversation that touched on background, interest in the role, and behavioral fit. The overall vibe was structured and fairly technical, with some emphasis on practical problem solving and some on how I think through uncertainty. I would describe the difficulty as moderate to hard, mostly because the probability questions were unusual and the statistics portion was fast paced. I didn’t feel like they were looking for memorized answers as much as clear logic and comfort with fundamentals. If you’re preparing for Goldman Sachs, I’d make sure you can explain probability trees and classic probability puzzles, and also be ready to answer regression questions quickly without needing to pause and think too long.
Prep tip from this candidate
Practice explaining probability trees and classic probability puzzles like Buffon’s needle out loud, since the final round leaned heavily on that style of reasoning. Also review regression concepts in a rapid-fire format so you can answer quickly under time pressure.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Goldman Sachs
Given that it is raining today and that it rained yesterday, write a function to calculate the probability that it will rain on the nth day after today.
| Question | |
|---|---|
| Recurring Character | |
| Find the First Non-Repeating Character in a String | |
| Rejection Reason | |
| Bagging vs Boosting | |
| Level Of Rain Water In 2D Terrain | |
| Google Maps Improvement | |
| Append Frequency | |
| Cyclic Detection | |
| Minimum Absolute Distance | |
| Comparing Search Engines | |
| Target Indices | |
| Production Model Monitoring | |
| How Many Friends | |
| SageMaker Deployment Architecture | |
| Impossibly Iterative Fibonacci | |
| Deciding Between Solutions | |
| Client Solution Pushback | |
| Company Acquisition Choice | |
| Alternative Vendor Tradeoff | |
| LRU Cache 1 | |
| Credit Score Estimation | |
| Using APIs for Downstream Tasks | |
| Direct Mail | |
| Risk Model for a Mortgage Bank | |
| Analyzing Multiple Data Sources | |
| 2nd Highest Salary | |
| Empty Neighborhoods | |
| Rolling Bank Transactions | |
| Employee Salaries |
Synthesized from candidate reports. Individual experiences may vary.
An early conversation focused on your background, interest in the Data Scientist role, and behavioral fit. This stage assesses whether you meet the baseline qualifications before advancing to technical rounds.
Selected candidates may complete a recorded video interview covering behavioral and motivational questions. This asynchronous screen is reviewed by the recruiting team before scheduling live technical interviews.
A live technical interview heavily focused on machine learning fundamentals and theory. Expect questions on loss functions and their derivations, logistic regression, and tree-based methods including boosting versus bagging, with emphasis on justifying your reasoning rather than reciting memorized answers.
A follow-up technical round continuing the ML and statistics focus, potentially including Python or SQL depending on the team. Interviewers look for clear conceptual explanations and the ability to discuss tradeoffs between model families and objective functions.
The final stage typically consists of two to five back-to-back interviews covering probability and math (e.g., probability trees, Buffon's needle), rapid-fire statistics and regression concepts, coding, business application of ML, and behavioral values. Candidates should expect to talk through their reasoning step by step under time pressure.