Other than a short stint at another firm as a big data consultant, my career before McKinsey was in academia. I studied physics at ETH Zurich, where I obtained my master’s and PhD. I then served as a visiting researcher at Princeton before returning to Zurich to do my post-doc work.
During that time, I started working on quantum machine learning, but I realized I wanted to use my physics knowledge for something more practical. It was great timing when a McKinsey recruiter contacted me to join the team the firm was building here.
I chose an internship at McKinsey instead of several cool opportunities in academia. I was so excited about the prospect of the internship I knew it was a sign I needed to do something different. So, in 2018, I left academia, and I am still incredibly happy with my decision.
Choosing consulting over academia
People who come from academia tend to like challenges or solving problems. I got a sense that’s what I’d be doing at McKinsey from the beginning. During interviews, it became clear people at the firm were solving real-life, complicated problems, and they were enjoying what they were doing. I knew McKinsey would offer me work that would constantly challenge me and the chance to work with people who were happy and motivated.
Three years later, I’ve found both things to be true.
My role in the firm
I joined McKinsey as a data scientist and quickly grew into a leadership role. Now, I’m an expert engagement manager, leading teams that are developing data analytics strategies and end-to-end solutions for clients. I largely support the manufacturing industry and work directly with the clients to solve problems around operations, quality, and working conditions.
Right now, I’m leading a study where we are developing a data analytics strategy for a large Consumer Packaged Goods company. We’re helping the company understand how to capture value out of analytics to improve sales and manufacturing processes. We’re looking at the technology, data, and people required to drive value for the business, make the supply chain more resilient, and improve operations.
Using my physics background to power McKinsey’s thought leadership
McKinsey is becoming a thought leader in quantum computing, especially as the technology has started to gain real momentum. Investments around the tech are rising and big tech players like IBM, Google and Microsoft are continuing to develop their quantum capabilities, with some already launching commercial quantum-computing cloud services.
To continue to drive forward our firm’s expertise in quantum computing, I tapped my physics background and joined some McKinsey colleagues to publish new research which lays out the use cases and possibilities for the technology in the coming years.
Along the way, we consulted with the McKinsey Technology Council, which consists of 60+ members, including scientists, engineers, investors, entrepreneurs, and others from external technology organizations and institutions, along with a dozen of our engineering, data science, and integrative consulting colleagues.
Relying on McKinsey proprietary tools
I use a range of technology to serve my clients, but two tools I turn to most often are solutions from QuantumBlack, McKinsey’s AI arm.
The first is Kedro, which is a library of code that enables data engineers and data scientists to create data and machine-learning pipelines. For example, for all our manufacturing clients, we write the code in Kedro, so it’s easier to develop models to improve the quality or throughput of a certain machine, and easier for our clients to build new solutions afterwards. McKinsey actually just donated Kedro to the Linux Foundation.
The second is CausalNex, a software library that data scientists use to analyze datasets and build models that consider cause-and-effect. With it, we can try to understand the causal connection relation between A and B and use that data to understand how to drive performance for our clients.
More about Matteo
At McKinsey, Matteo is active in recruiting new employees. He is also a member of various support groups for parents, and he participates in the data science and engineering guilds. Outside of work, he spends all his time enjoying kid-friendly activities with his family.