Before joining QuantumBlack, AI by McKinsey as a data scientist specialist, I was more on the research side. My undergraduate degree is in genetics, and then I got my PhD in computer science at Oxford.
From there, I went on to work at two startups, the first of which spun out to a second company, and I became the Chief Scientific Officer/Chief Operating Officer there, focused on research and operations. We sold the company, and it became publicly traded, which was a huge achievement for us. Still, I ultimately left the company because I wanted to expand my horizons. My job had been focused on only one type of research and industry, and I was bored.
McKinsey is a big landing spot for Oxford graduates, so I knew people who had joined the firm. I had gotten some insight on what McKinsey was doing in the tech space, and I even served on a panel that was organized by QuantumBlack, so I was familiar with the work there.
It felt like it was the ideal fit for me. It is an global firm with a great reputation, but mostly, it would offer me broad experience. I loved building those startups, but it wasn’t the most efficient way for me to learn how business and the market works. I joined McKinsey a year ago because the firm offers me access to completely different business models and companies. In a shorter timeframe, I can help more companies grow—sometimes several in a year—versus spending five or more years on one organization.
My role at the firm
I am a data scientist specialist. My background is heavily in digital health, and I want to continue on that path of working with healthcare startups and biotech companies. I focus on implementing and delivering solutions, which involves building the proper architecture and implementing DevOps and MLOps. Of course, we’re building the AI/ML algorithm, but it’s not just that. Seventy to ninety percent of code base and everything that surrounds the algorithm is not the machine learning model. But it’s vital to the solution because it allows the model to be alive and continue the life cycle, and it all must be constantly updated and fed new data.
I am still hands on with the technology, as far as writing code and that sort of thing, and I enjoy doing that. However, I am also responsible for deciding which technology we use and how we handle data security and data privacy. I must be highly aware of what clients need and what tech is in the market to do my job well.
Seeing the results of my work
One of my most meaningful projects so far was my first. I worked on this incredible team, and we clicked immediately and did really great work. We were working on a project using a McKinsey platform that allows you to deliver a digital solution to the client within a matter of hours. For example, if you have a new forecasting algorithm, you can easily ship it to end users super-fast. We can adapt or retrain the underlying machine learning model so it can fit different businesses, models, and geographies, but most of the code is already there.
It was exciting because we secured a contract with a client because of the capabilities the technology offers, and it felt great to have that kind of impact on my first project.
Accelerated impact is one of the reasons McKinsey is such a great place to build a career in tech. You work on a wide range of new products, but you also see the results of that work much faster and how your efforts actually impact users. Often, when you work in tech, you don’t see the end users, and you lose that connection and reason for developing a product. At McKinsey, I am working with clients, and I see how our solutions benefit them in real time. To me, that is incredibly satisfying and makes the work easier.
More about me
I will be making the move from Kazakhstan to Canada soon, and I am super excited about that. In my free time, I’ve really gotten into windsurfing. It’s a sport but it’s also highly meditative. You must be in control of your body and mind, and when you get a strong wind, you experience this amazing adrenaline rush.