Before joining McKinsey, I had some reservations about the technical level consultants could reach during analytics projects. My first project was an eye-opener. I tested and employed bleeding edge machine learning techniques from the Natural Language Processing domain to help our clients radically improve their business.
Building my consulting toolkit
Every day, I work alongside highly competent teammates and, together with our clients, we tackle their most pressing problems. My colleagues have taught me to organize information and manage tasks under time constrains and helped me build effective communication skills. I’ve learned these skills are essential to converse across disciplines and maintain a collaborative environment.
Diving deeper into my passion for risk analytics
Another great thing about working at McKinsey is the broadness of possibilities. For example, with a background in quantitative finance and risk, I was naturally interested in McKinsey’s Risk practice after I joined as a data scientist. My Stockholm-based analytics colleagues have encouraged me to explore the function and helped me to take the right steps to get closer to the area.
In just a few weeks, I was attending a risk and analytics training program, aimed at improving our breadth and depth in quantitative risk areas, including machine learning techniques. Moreover, I now have two mentors from the Risk practice who provide advice and guide me in my career journey.
Find a job like Spilios'
Spilios is a data scientist based in Stockholm. Prior to joining McKinsey, he worked as a senior data scientist for Ferratum Group bank in Sweden and as an income analyst for Alpha Bank Greece. Spilios has a bachelor’s in mathematics from University of Athens, a master’s in applied statistics from Brasenose College in the United Kingdom and a Ph.D. in statistics from Imperial College London.
For more information on McKinsey's data science career paths, visit mckinsey.com/TechCareers.