Earning Her Experience 

It would be understandable if Maddie Smith (MSiA ‘22) took a few deep breaths when she sat down to work in January on her first day as a data scientist at McKinsey & Company.  

The previous five years had been an educational whirlwind as she earned her bachelor’s degree in statistics and data science, and a certificate in managerial analytics. 

Her December graduation from Northwestern Engineering's Master of Science in Analytics program (now known as the Master of Science in Machine Learning and Data Science (MLDS) program), was the final step she felt she needed to launch what she expects to be a long and successful career working with data.   

“I was aware that since I entered the program straight after undergrad, my lack of work experience could be my main barrier to succeeding after graduation,” she said. “Enrolling made it possible for me to get both the education and work experience that I needed at the same time.”  

Now three-quarters of a year into her career, Smith is reaping the benefits from her dedication to education. Her classes gave her the knowledge she needed to find early success with McKinsey, a global management consulting firm; the program’s outside-the-classroom offerings such as the practicum, capstone, and internship requirement gave her the experience.  

“What I learned has enabled me to take more ownership of my work and deliver more value to my clients,” Smith said. “In school, it can be easy to focus on getting a perfect score, but because we were often evaluated on our approach to the problem, I learned how to structure my thinking more effectively and ask better questions to create business value.”  

That value is paying immediate dividends. Armed with knowledge and experience in machine learning, Smith has already tackled projects for a wide variety of McKinsey clients.  

One of the first opportunities involved using geospatial analytics and machine learning to optimize a store fleet. She also has designed and implemented a data governance strategy for a large digital acceleration project.   

“For me, there really is no average day because it depends on the problem we are trying to solve and my role within the team,” Smith said. “I like the ability to work on a wide variety of problems and use a wide array of skills.” 

The opportunity to build such a large and diverse toolkit was a part of what attracted Smith to the program as she neared the end of her undergraduate years at Northwestern. She said the program’s unique curriculum, variety of projects, and small cohort size made it the obvious choice for her.  

“The curriculum takes a holistic approach,” she said. “It places an additional emphasis on developing business acumen and being able to communicate insights well.”  

During her time in the program, Smith interned at Enova International, a consumer lending company. The experience helped her advance her machine learning skills in a real-world setting and highlighted what she wanted to look for in a workplace environment.  

That lesson was important, as were the countless takeaways she garnered from the program's faculty. Just as key to her education, however, were the lessons she learned from her classmates — students who made her experience even more rewarding. 

“The cohort itself contains a wealth of diverse experience and knowledge to pull from,” she said. “Your classmates can help with anything from finding the best boba in Evanston to getting through a late night of homework to nailing your first technical interview.”