Goldman Sachs Data Scientist Interview Questions + Guide 2024

Goldman Sachs, a leading global financial institution, remains a powerhouse in investment banking, securities, and investment management services. Since its founding in 1869, Goldman Sachs has built a strong reputation for driving innovation and excellence within the financial industry.

If you aim to join this firm, Interview Query is here to guide you through every step. Our guide covers the interview stages, common Goldman Sachs data scientist interview questions, and invaluable tips to help you succeed. Let’s dive in and get you ready for your Goldman Sachs Data Scientist interview!

The interview process usually depends on the role and seniority, however, you can expect the following on a Goldman Sachs Data Scientist interview:

Recruiter/Hiring Manager Call Screening

If your CV makes it through the initial screening, a recruiter from the Goldman Sachs Talent Acquisition Team will reach out to verify key details like your experience and skill level. Behavioral questions may also be part of this screening process.

In some cases, the hiring manager for the Data Scientist role might be present during this call to answer your queries about the role and the company. They may also engage in surface-level technical and behavioral discussions.

This initial call typically lasts about 30 minutes.

Technical Virtual Interview

Successfully passing the recruiter round will grant you an invitation for a technical screening interview. This stage usually involves a virtual interview via video conference and screen sharing. Questions during this 1-hour interview may cover Goldman Sachs’ data systems, ETL pipelines, and SQL queries.

For the Data Scientist role, it may also include take-home assignments related to product metrics, analytics, and data visualization. Your proficiency in hypothesis testing, probability distributions, and machine learning fundamentals might also be analyzed during this stage.

Depending on the seniority of the position, you might also be given case studies or real-world problem scenarios to solve.

Onsite Interview Rounds

Following another call with a recruiter to outline the next stage, you will be invited to attend onsite interview rounds. These rounds will vary depending on the role but will generally involve multiple interviews throughout the day. Your technical abilities, including programming and machine learning modeling skills, will be evaluated thoroughly.

If you were assigned take-home exercises, you might need to present your work during the onsite interview.

Typically, interviews at Goldman Sachs vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

1. Create a function rain_days to calculate the probability of rain on the nth day after today.

The probability that it will rain tomorrow depends on whether it rained today and yesterday. If it rained both days, there’s a 20% chance of rain tomorrow. If it rained one of the days, there’s a 60% chance. If it rained neither day, there’s a 20% chance. Given it rained today and yesterday, write a function to calculate the probability of rain on the nth day after today.

2. How would you provide rejected loan applicants with reasons for rejection without access to feature weights?

Suppose you have a binary classification model that determines loan eligibility. As a financial company, you must provide each rejected applicant with a reason for their rejection. Given that you don’t have access to the feature weights, how would you generate these reasons?

3. Are we overestimating or underestimating the actual population’s credit score using a fixed cutoff?

Assume you have a credit model with a calibrated score for creditworthiness, with a small margin of error. If the model estimates a score of 83%, the actual score likely falls between 81% and 85%. By using 83% as a cutoff for creditworthiness, are we overestimating or underestimating the actual credit scores of the population?

4. How would you design an ML system to extract, transform, and store data from Reddit and Bloomberg APIs for downstream models?

As a machine learning engineer for a large bank, you have access to the Reddit API for finance and news-related subreddits and the Bloomberg API for daily stock prices. How would you design an ML system that extracts data from these APIs, transforms it, and stores it in a format usable by downstream modeling teams?

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Goldman Sachs data scientist interview include:

• Be Prepared for Coding Tests: Goldman Sachs places a significant emphasis on technical proficiency. Brush up on data structures, algorithms, and coding practice through Interview Query to prepare for coding assessments.
• Understand Business Use Cases: Goldman Sachs looks for candidates who can bridge the gap between data science and business applications. Be ready to discuss how your technical skills have solved real-world business problems.
• Master Behavioral Questions: Apart from technical skills, company culture and fit are crucial. Prepare to answer behavioral questions that demonstrate your teamwork, leadership, and problem-solving abilities.

What is the average salary for a Data Scientist at Goldman Sachs?

Q: What is the culture like at Goldman Sachs for Data Scientists?

The culture at Goldman Sachs is known to be demanding yet rewarding. It’s essential to have a strong understanding of the technical aspects, as well as the capacity to work collaboratively. However, there have been reports of lengthy and sometimes disorganized interview processes, so patience and persistence are key.

Q: What are some common responsibilities for a Data Scientist at Goldman Sachs?

Responsibilities include applying knowledge in SQL, Python, or R to solve business problems, optimizing processes, and communicating complex analytical concepts clearly. Data Scientists collaborate with cross-functional teams to derive actionable insights and support marketing and capital allocation decisions.

Interviewing for a Data Scientist position at Goldman Sachs is a rigorous process that challenges candidates on various fronts. Aspiring candidates can expect a series of coding assessments, technical phone screens, behavioral interviews, and rigorous onsite evaluations. Despite the intense nature of the interview process, those who succeed often highlight the opportunity to work on impactful projects and the chance to join a team of highly intelligent professionals.

If you want more insights about the company, check out our main Goldman Sachs Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Goldman Sachs’ interview process for different positions.

Good luck with your interview!