Curiosity and the promise of new challenges drive me to learn what new problems I can solve with data science. I’m just like my kids–once they get a hammer, they seize every chance to explore what else they can build.
As a McKinsey data scientist, I solve so many problems from different industries that my curiosity never ends. I focus mainly on consumer-facing industries, like media, retail, and grocery, because I like seeing how the products we build help customers. For example, I’m currently working on a grocery supply chain project. I feel fulfilled and excited to be working toward something that will make a difference in people’s lives. I never feel bored when I’m facing tough, rewarding challenges.
Worldwide connections
I clearly remember my first project at McKinsey, and how generously my global colleagues offered me help. We were working with a grocery client to build a personalization model for a mobile app that would tailor shopping recommendations to improve sales and the customer experience. Grocery is a competitive industry where companies struggle to stand out among competitors, so we needed the app’s recommendations to be highly relevant to customers’ needs.
During the project, I experienced a technical challenge with the cloud server when trying to deploy a personalization model in Azure. As a new hire, I had experience with Amazon Web Services (AWS) and other cloud services, but not with Azure, which has a unique way of deploying models.
With no clue how to solve the challenge myself, I reached out to our global data science community and described my challenge. After a few hours, I received an email from a colleague who could help. He is based in our Silicon Valley office, thousands of miles away from where I live. One of the fantastic things about McKinsey is that we’re a global company with 4.5K+ talented tech colleagues on all continents.
Whenever I am facing critical challenges, I know someone somewhere in the firm has tackled similar problems before, and will generously offer their help. Supporting each other is part of the McKinsey culture. With my colleague’s help, I solved the challenge with Azure and we built a successful application.
Multi-disciplinary problem-solving
Before joining the firm, I thought deep tech skills always lead to successful products. Now I see tech is only part of the story. It’s a necessary enabler, but there are many other critical elements, like mindset, diversity, and stakeholder development.
Since I’ve been at McKinsey, I’ve learned how to solve business problems with technology and manage processes from beginning to end. I can work effectively on a multi-disciplinary team, adapt to clients with different working preferences, communicate data findings, and take tangible actions.
As a management consulting firm, this is McKinsey’s strength. It’s the perfect place for data scientists to advance their technical skills, learn soft skills, and grow quickly.
Outside of work
I am a proud father of three daughters, Evelyn, Nina, and Melissa. I love doing almost everything with them, from hiking in the mountains, to visiting the museums, to playing Nintendo Switch. I am inspired by my daughters’ curiosity and how they explore the world, and I enjoy being part of it.
I feel very supported by the firm. From extra paternity leave I received to support my wife when she is back to work, to the detailed guidance on how to educate kids during the Coronavirus when schools are off.
About Ninghang
Ninghang is a data science expert with McKinsey’s Amsterdam office. Prior to joining McKinsey, he was a data scientist at Booking.com, where he focused on personalization and machine learning. Before that, Ninghang was a visiting researcher at the University of California, Berkeley. Ninghang earned his master’s in artificial intelligence and Ph.D. in machine learning from the University of Amsterdam.
For more information on McKinsey's data science career paths, visit mckinsey.com/TechCareers.