Continuing to Learn, Even After Graduating

Narin Dhatwalia spent more than two years working in India as a data scientist and business analyst before enrolling in Northwestern Engineering's Master of Science in Analytics (MSiA) program, now known as the Machine Learning and Data Science (MLDS) program. 

With his previous experiences, Dhatwalia (MSiA ‘22) was more interested in learning the nuanced aspects of data science and machine learning, rather than basic introductory information. 

MSiA gave him just what he was looking for — and more. 

Today, Dhatwalia is a senior analyst for data science and analytics at consumer credit reporting agency TransUnion. His job is to provide a framework from mountains of data to help banks, credit card companies, and other lenders make wise choices when weighing whether to loan money.  

MSiA prepared him to excel in his current role, he said.  

"Some technical areas in which I really wanted to build further expertise included real-time model hosting on Amazon Web Service, becoming adept at using Git and the Linux terminal, understanding Container technology via Docker, and getting hands-on exposure to big data tools like Hadoop and PySpark," he said. "Needless to say, MSiA delivered on all my expectations and made me feel much more confident in each of these technical areas." 

Dhatwalia completed his MSiA internship at TransUnion, where he built a survival analysis model — something he had never heard of before MSiA. He credited his first quarter Predictive Analytics 1 course with giving him the theoretical foundation necessary to complete the model and thrive in his internship. 

TransUnion collects information on more than one billion consumers across 30 countries, including more than 200 million people in the United States. The company, along with Experian and Equifax, is one of the three biggest credit agencies.  

Dhatwalia's job is to take that data and turn it into actionable insight, both for TransUnion and the institutions that rely on its services. One of his projects involved feature creation for a new financial product; these will add a new facet to consumers that lenders may take into account during the course of business. 

He recently also designed and developed a new risk score that rank-orders consumers when they apply for a closed-end installment loan. 

Dhatwalia was drawn to MSiA because of its small cohort size, the caliber of its full-time faculty, and the flexibility to pursue courses of particular interest. In Dhatwalia's case, that meant taking an applied mathematical statistics course usually meant for PhD students in Northwestern Engineering's Department of Industrial Engineering and Management Sciences. 

“I reflect upon my time at Northwestern as a challenging phase of my data science journey where I tackled many novel academic challenges and eventually overcame them,” Dhatwalia said. “That experience gave me the confidence to look at any new framework, tool, or technology from the perspective of an experienced student who knows he can attain mastery over another wave of disruptive technology as long as he shows the same discipline and passion.” 

Those disruptive technologies are consistently infiltrating the data science field. That is why Dhatwalia is thankful that MSiA instilled in him the mantra to continue learning — a lesson he prides himself on today. 

“My learning cannot stop after MSiA," he said. "The life of a data scientist is always full of new learnings, and maintaining a curious mindset is key for long-term success.”