The McKinsey QuantHub Test for Data Science Consulting Roles

If you’ve got a background in data science or AI and an interest in solving tough business problems, a position at McKinsey Digital, QuantumBlack or on their Risk team could be right for you.

To interview for either of these positions, you’re first going to have to pass the McKinsey QuantHub test.

This test is quite different from ones you’ll have taken when applying for other data science positions, but don’t worry, we can tell you what to expect.

In this article, we’ll discuss:

What the McKinsey QuantHub test looks like,
Questions you can expect,
How you should prepare for the McKinsey QuantHub test,
Tips for passing the QuantHub test,
What groups in McKinsey use the QuantHub test as an assessment tool,
Other consulting firms to consider if you’re a data scientist, and
How to Prepare for Your In-Person Interviews After You Pass the McKinsey QuantHub test.

Let’s get started!

The McKinsey QuantHub Test – What It Looks Like

Candidates have 72-100 minutes to complete a series of questions on programming and statistics.

There are 3 test sections with approximately 12 questions per section.

Candidates are asked to choose from multiple choice answers rather than write code to demonstrate their knowledge.

The 3 sections to the McKinsey QuantHub test cover:

R coding.
Python coding. This section includes NumPy and scikit questions.
Statistics. This section includes T-tests, Anova regression distributions, and chi-square.

The number of questions is not set in advance. The test is adaptive and the more questions you answer correctly, the more difficult your next questions will be. So, if you see harder questions this is a good sign.

You are not expected to be equally skilled in all 3 sessions of the test.

You’ll be asked your skill level on each section of the test before you begin it (1-5 out of 5). The test will then provide you with questions tailored to your skill level.

McKinsey QuantHub Questions You Can Expect

The test consists largely of data science conceptual questions.

In the R section, importing data, cleaning data, and data structures are covered.

In the Python section, data mining and machine learning are covered.

Sample questions:

What is the output of this line of code?
Which statistical methods require that the residuals be normally distributed but not the raw data for n=20?
What do you predict the next data points in this time series will be?
What do you predict the results of this regression will be?

How to Prepare for the McKinsey QuantHub Test

When invited to take the test, you’ll be given 2 links. One link will direct you to practice questions you can use to prepare for the test, and the second link takes you to the actual test.

We suggest you take the time to complete the practice questions. This will ensure you know what to expect when you take the actual test and it will help you to identify topics on the test that you may need more preparation for.

5 Tips for Passing the QuantHub Test

1. Use the practice questions to familiarize yourself with the test.

There are fewer questions in the practice test so you won’t be able to practice managing your time. However, you will become familiar with the types of questions asked.

2. Keep RStudio and your preferred Python IDE open while taking the test.

Some questions ask you to take a dataset that’s available in R, manipulate it, and choose the correct output from the multiple choice options. Having the IDE open will save time if you want to run the

3. Pay attention to time.

Some applicants report the timing on the test is tight, while others report having plenty of time. Play it safe by not spending too long on any one difficult question.

4. Make sure your family and friends know that you’ll be taking an assessment so you aren’t interrupted.

Disruptions during the test waste time and create stress. Prevent them if at all possible.

5. Make sure you have good Internet connectivity for the time period you’ll be taking the test.

If you don’t want to accidentally be disconnected.

What Groups in McKinsey Use the QuantHub Test

McKinsey Digital, McKinsey QuantumBlack, and the McKinsey Risk Team use the QuantHub test as part of their application and interview process.

Other Consulting Firms to Consider if You’re a Data Scientist

If McKinsey’s data scientist consulting roles looks like a good fit for your background and interests, here are some similar consulting positions to consider:

BCG Gamma,
Oliver Wyman Labs,
Bain Advanced Analytics Group (AAG), and
Accenture Analytics

How to Prepare for Your In-Person Interviews After You Pass the McKinsey QuantHubTest

If you successfully complete the QuantHub test, McKinsey’s Recruiting department will schedule a call with you to discuss next steps in the recruiting process and coaching sessions.

Applicants who will move on to in-person interviews will be scheduled for coaching sessions before these interviews.

The in-person interviews will follow the typical consulting interview format and cover case study questions and personal experience questions. See our Ultimate Guide to Case Interview Prep and or article on McKinsey PEI Questions for more on preparing for the in-person interviews.

6 Tips Data Science Applicants Should Know About Case Interviews

Case interview questions for applicants to QuantumBlack and the Risk Team often have some programming or statistics component.

However, it’s important to remember that consultants aren’t hired to just solve coding or statistics problems. They’re hired to solve business problems.

To pass your case interview, you’ll need to focus on the big picture business problem. If you provide a perfect answer on the data analytics aspect of the case but can not relate the insights back to how to solve the business problem, you won’t pass the interview.

To move on from the case interview and land an offer, you need to:

Understand the business problem that needs to be solved,
Develop a structured approach to addressing all key aspects of this business problem,
Run analysis needed to get data on the problem,
Identify a solution,
Show that you can implement the solution, including identifying risks that may be encountered during implementation and how to avoid them, and
Present your solution persuasively.

1. Understand the business problem that needs to be solved.

The client in a case question may be trying to increase profits, reduce costs, make their operations more efficient, or decide whether or not to acquire another company. The answer to their problem will not be the result of data analysis, but what the data analysis means they need to do to improve their business performance.

2. Develop a structured approach to addressing all key aspects of this business problem.

Data analysis will be one thing that needs to be done to solve the case, but take a moment to consider other aspects of the business problem that need to be addressed. These will include things such as competition, pricing, customer preferences, etc. Make sure you understand all aspects of the problem before diving into one too deeply so you can split your interview time into addressing all relevant issues.

3. Run analysis needed to get data on the business problem.

For data analysts, this is often the most straightforward part of the case. Don’t focus only on this.

4. Identify a solution to the problem.

Based on what you’ve learned about the client’s business and the market they compete in, as well as the results of your data analysis, you’ll need to make a recommendation on what action the client should take.

5. Show that you can implement the solution, including identifying risks that may be encountered during implementation.

Many types of implementation problems can crop up when you try to change a business. They include:

Convincing key stakeholders it’s the right thing to do,
Getting needed resources (financial, people, equipment, etc.),
Training staff on new technology or processes,
Communicating changes to customers,
And many more.

For your client to solve their business problem, they need to not only know what they should do, but also be able to make needed changes to their organization. Identifying implementation risks ahead of time is key to overcoming them, so identify any you think are relevant.

6. Present your solution persuasively.

Communication is a key part of a consultant’s job. Having the right answer is not enough, you need to persuade the client to take action, so show you can do this in your interview.

In this article, we’ve covered:

What the McKinsey QuantHub test looks like,
Questions You Can Expect,
How to prepare for the McKinsey QuantHub Test,
Tips for Passing the QuantHub Test,
What Groups in McKinsey Use the QuantHub Test,
Other Consulting Firms to Consider if You’re a Data Scientist, and
How to Prepare for Your In-Person Interviews After You Pass the QuantHub test.

Still Have Questions?

If you have more questions about the McKinsey QuantHub test, leave them in the comments below. One of My Consulting Offer’s case coaches will answer them.

Other people prepping for the McKinsey QuantHub test found the following pages helpful:

Help with Case Study Interview Prep

Thanks for turning to My Consulting Offer for advice on consulting interview prep. My Consulting Offer has helped almost 85% of the people we’ve worked with get a job in management consulting. We want you to be successful in your consulting interviews too. For example, here is how Chenbin was able to get his offer from McKinsey.

2 thoughts on “The McKinsey QuantHub Test for Data Science Consulting Roles”

Shikha Singhal

April 23, 2022 at 1:05 am

Need your advice to Crack McKinsey interview
- Rebecca Smith-Allen
  
  May 2, 2022 at 6:58 pm
  
  Our Guide to Case Interview Prep or our article on the McKinsey Case Interview will help you.