Is there any differences between McKinsey Digital interview and McKinsey?

Hi there,

For consultant roles, the process is the same but with a higher likelihood of cases related to digital. If you are not interviewing for a consultant role, the process may be different.

Hope this helps,

Francesco

Hello!

Usually, when interviewing as a generalist consultant for McK Digital vs. the traditional McK, there are going to be some differences, particularly in the fact that for sure at least 1 of the interviewers will be from Digital, which has implications in the FIT and case. 

However, as a UX designer, I am almost sure they will test your abilities in a technical and capabilities way, not a business case. 

Hope it helps!

Cheers,

Clara

First, learn standard cases - the methodologies and skills you will learn here will be universally applicable.

Second, make sure you understand the digital world well. Some topics include (but are not limited to):

• Cloud
• Security (encryption basics, cloud security)
• Agile
• M&A and IT
• Legacy systems, merging systems, system upgrades
• Digital transformation
• IoT

Third, focus on digital-based cases

These two that I've created should get you started :)

https://www.preplounge.com/en/management-consulting-cases/interviewer-led-mckinsey-style/advanced/mckinsey-digital-bcg-platinion-oil-gas-upstream-technology-196

https://www.preplounge.com/en/management-consulting-cases/candidate-led-usual-style/intermediate/yodaphone-195

Hello there!

Depending on the role you are interviewing for, the interview process may differ.

1. In case it's a consultant role, it would be the same as for the generalist track, so prepare for

• McKinsey-style case interviews
• McKinsey PEI questions
• PST just in case ​  

2. ​For the specialist roles there are function-specific interviews, so they could be technical questions (e.g. for software engineers) or testing your date science ability (e.g. for data scientists). But PEI questions would be included as well.

Here's some more information on this topic 
https://www.preplounge.com/en/consulting-forum/how-to-prepare-for-digital-mckinsey-2032

Was it helpful?

Cheers,
GB

Hi,

McKinsey Digital interviews are not that much different from McKinsey Generalist interviews. Specifically, McKinsey digital is working on IT strategy, Agile transformations, etc. It also depends on the country where you apply - while in US you can expect a lot of Digital projects, in other countries most of your projects will be in various industries and functions.

All of the above has the following implications for the interview process:

• You should prepare similarly to the regular McKinsey interviews (i.e. cases and fit parts)
• Additionally, you should be ready to solve specific cases related to IT (e.g. agile transformation cases. The case process itself will be very similar to the regular Mck interview)
• You should be ready to talk about the relevant IT topics (Hint: I recommend reading the articles on the McKinsey / BCG websites. Search for "agile", "Digital", etc)
• 1-3 of the partners on your interviews will be from McKinsey Digital. You can collect some information about interviewing with them in advance and prepare the interesting questions.

I have not seen any digital cases online. But here is a list of potential topics you need to know. It is a full list and looks scary but they test a lot of these concepts depending on the particular role:

• Cloud (cloud vs own servers pro and cons, uptime % SLAs, load balancing, etc)
• Security (encryption basics, password change policy, cloud security and encryption on your computer)
• Agile and development (Agile principles)
• M&A and IT (Will it be one system or two systems? Is it "Adopt and go" approach or we take the best of both systems and create a new one? Integrate now, innovate later? Or Innovate now, when you have time?)
• IT strategy and Digital transformation (Similar to M&A)
• IoT (Mainly the use of IoT in operations - Process steps completion, measuring temperature, maintenance measurements, other measurements, predictive algorithms, etc)

Best,