An Interview with Constantin Weisser


This is an interview with Constantin Weisser who will join QuantumBlack, a McKinsey Company, as a Data Science Consultant in 2021. Prior to joining McKinsey, Constantin completed his Ph.D. in Physics, Statistics, and Data Science at MIT and took part in multiple consulting case competitions. We asked him about his decision to become a data science consultant and the interview process.

Q) What is data science consulting and how is it different than general consulting?

Data science consulting (DSC), at least the way I will use the term, is quite different than general consulting. Most data science consultants have a background in technical subjects and advanced degrees. Their time is split between talking to clients, cleaning data, and implementing machine learning models in code. DSC is one step more technical than business intelligence and this focus is reflected in the interview process.
Still, there are a lot of common elements in the two different types of consulting. Engagements tend to be short, client interactions are vital, and traveling is expected. Overall, it is the best career path to learn about different industries, business fundamentals, and the forefront of AI.

Q) What is your background and what post-PhD career options did you consider?

As a PhD student in Physics, Statistics, and Data Science, my research focused on gaining insight into dark matter from a 1000-person particle physics experiment at the Large Hadron Collider at CERN. As part of that work, I analyzed terabytes of dirty and disparate data, set up processing pipelines, and developed purpose-built machine learning (ML) algorithms. I wanted to pursue a job after graduation that was heavy in ML. For that reason, I considered careers in three different industries: 1. Tech Companies (ML research or data science positions) 2. Finance (quantitative research positions) 3. Consulting (data science positions).

Q) How did you decide on data science consulting?

All three paths would have allowed me to become part of a multi-disciplinary and high-caliber team to create solutions with ML. However, ML research in tech is difficult to break into if you don’t have a pure ML PhD. I entertained the idea of joining a hedge fund but decided that work with real impact created by the company I would join was a priority for me. After doing an internship as a research scientist at Amazon Alexa Health NLP, I learned that data science in tech was generally a good pathway for me. However, interpersonal, presentation, and business skills were not as valued as I had hoped. That’s when I decided that DSC would be the perfect balance between machine learning, interpersonal skills, and business acumen. All of this exploration clarified that my goal is to learn about how the world works and improve it. DSC was my pathway to achieving that goal.

Q) What is special about QuantumBlack (QB)?

There is a common problem in DSC: some clients have competencies in machine learning and others do not. The former are not likely to hire consultants and the latter often do not know if their business problem needs advanced ML. Small DSC firms can struggle to find engagements where advanced analytics is essential. Big firms often don’t have highly ML trained teams or don’t give them enough autonomy.
QB has the best of both worlds. It can tap into McKinsey’s client network but has the autonomy to turn down projects and reduce consultant travel. In addition, QB performs cutting-edge causal inference R&D that consultants can contribute to. Everybody is approachable and collaborative and there is a dedicated team of data engineers that allow data scientists to concentrate on what they do best: experiment and build models.

Q) How was the QB interview process?

First, I had to tackle a two-hour coding assessment that was similar to the tech firm challenges seen on LeetCode. It tests computational thinking, familiarity with scripting languages and knowledge of machine learning libraries. This was followed by a 30-minute screening interview filled with rapid-fire technical data science questions. In the third stage, a 90-minute data science case study interviewer asks the interviewee to describe how they would tackle the experimentation and data science modeling of a real-world case. The final stage consists of three more interviews. In the first, you have to give a clear and approachable technical presentation about your past work. This is followed by a personal experience interview that is structured like a general McKinsey experience interview. For example, you could be asked about a time you had to convince someone to do something, or the time you led a team. Finally, a 60-minute technical experience interview with ML experts will probe the limits of your understanding of ML.
Overall, the interviews select people with technical expertise and proven interpersonal, presentation and leadership skills. If you’re interested in pursuing DSC, or even joining QB, feel free to reach out to Constantin Weisser.

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