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McKinsey Analytics teaches skills that go with you

Read more about: Analytics

What is your area of specialization?

CRM, promotion analysis, supply chain optimization, pricing, prediction models – you name it, I worked on it. During my last year, I focused on predictive maintenance, which was an evolving topic at that time. Now, as head of data science at L’Oréal, I cover a range of topics, such e–commerce transactions, customer segmentation, and promotions.

How did McKinsey Analytics prepare you for your current role?

I got to know so many different sectors and functions working in analytics. I learned and applied many different analytics methods and tools. I also learned to handle unexpected surprises and roadblocks on projects and manage my way around them, while staying focused on the project’s success.

Describe one thing you wouldn’t have done or learned without a McKinsey Mentor.

I received an interesting job offer about two years into my McKinsey experience. I discussed it with Holger Hürtgen, who was a senior expert in the Marketing practice and one of my mentors. He helped me think through the trade–offs, and in the end, I stayed another four years. It was the right decision.

What is the biggest difference in a product company vs. consulting?

Niklas inline

There are more similarities than differences. Cosmetics is a very dynamic business, and L’Oréal is very entrepreneurial. For example, I initiated an event in our office to kickstart collaboration, and we are about to launch new software tools and platforms. I am also in a role in which I do some internal consulting for our brands.

Why would you recommend data scientists apply for McKinsey Analytics?

It’s the best place to learn best practices and get a variety of experiences. No other place has such a steep learning curve when it comes to applying data science methods to real world problems. There is also a very good international network.

Learn more about McKinsey Analytics here

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