– While I was in graduate school in the US, McKinsey’s German office held a career session for German students like me. It sounded very exciting to become part of the firm, but I wasn’t sure if consulting was the right fit for me. I wanted a job where I could leverage my education in math and statistics and I didn’t expect to find that at McKinsey. While browsing the website, I came across a posting for an analytics role and knew it was the perfect job. The opportunities to leverage data and analytics to make real impact for clients and accelerate my professional development were extremely compelling and exciting.
The most exciting thing about Analytics at McKinsey is that I’m at the center of a huge wave of change going through the firm and the industry more broadly. All of our clients are interested in analytics, so our practice is growing rapidly in people, capabilities, and the solutions we bring to our clients. I’m heavily involved in building out our team of data scientists in North America, which means I’m very active in recruiting. I’ve probably interviewed more than 200 candidates in the last few years; I love those conversations because they let me get to know each interviewee’s strengths and think about how they fit into our team.
Similarly, the diversity of people on my teams is inspiring. On one recent engagement with a pharmaceutical company, my team consisted of:
- An engagement manager who coordinated all of the pieces of work we did
- Three generalist associates, each responsible for leading the work on a different treatment area
- A full–time statistical modeler who predicted physician behavior
- Three data engineers who analyzed data from our client and various third party sources
- Three experts who supported our team part–time by providing guidance on the analytics and domain knowledge
Having this variety of expertise in our team room meant that we got to the best possible answer with the client. By combining advanced analytics and the ability to dive into the data with deep pharma knowledge, we were able to help the client allocate their national marketing and sales budget in a much more effective way, and target the most promising opportunities for growth. I once read that effective data science is about knowing which bolt to tighten, not having a fancy wrench – meaning practical experience and iterative problem solving is much more important than knowing a lot about machine learning algorithms you can’t apply in the real world I learned a lot along the way – as did everyone else on my team. In each iteration we refined our approach and I have been able to build on this experience in other projects since then. We were very collaborative and had a lot of fun, capping off a successful project by taking out the whole team to view a major league baseball game of our client’s home city team.
Outside of work, I play chess competitively, so on weekends I like to play in tournaments at the local chess club. I also squeeze in some quick online games or read about opening theory whenever I can. I enjoy the competitive nature of the game; it’s a battle of ideas in a fun, casual way.
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