Like many data scientists, Jason Rosenblum did not start his professional life knowing he wanted to become one.
While at Tulane, he majored in finance and went on to receive his MS in accounting, enticed by the field’s steady need for qualified practitioners. There was just one problem: about a year after he entered the workforce, he realized he had no passion for that which he had been trained to do.
“I took my CPA tests and was set to start my career—then I realized I wasn’t really interested in the work,” Rosenblum recalls
A fortuitous conversation with a family friend led Rosenblum to data science, specifically the data science tools used in the accounting field. “The big four accounting firms have been introducing analytics to their auditors, and I became really interested in that area,” he says.
Not two years later, Rosenblum works as a data analyst for financial services giant PayPal while pursuing his Master of Science in Analytics (MScA) part-time.
“The most challenging part of the program is balancing it with work,” he says. “Six hours of class plus however many hours of homework and projects and forty-plus hours of work a week is a lot—but it’s manageable.”
Although this time commitment appeared daunting at first, Rosenblum has excelled at both his studies and his career. He has some advice for other working, part-time students who want to get a jumpstart in their chosen field.
Narrow your focus
Before he began the part-time MScA program, Rosenblum knew what kind of data scientist he wanted to become, which enabled him to limit his job search to analyst positions in the financial sector. He advises fellow students to do the same in order to maximize the use of their time.
“Start looking into products that you're interested in so that you have a good idea of what you might want to focus your efforts on,” he says. “Class projects, data sets—there are so many resources online.”
Talk yourself up
After narrowing his focus, Rosenblum used his acceptance into the MScA program as a tool to entice potential employers in his chosen field.
“Being able to say that I'm in this program adds credibility and gives me an advantage over someone who says that they want to do data science and machine learning but hasn't made any concrete steps,” he says. “When I made connections, I emphasized that the program is more advanced than others, and an employer was willing to take me on because I had taken the step of joining a master's program.”
As soon as Rosenblum started applying to data science master’s programs, he started looking for a job in the field. Around the time he was accepted to the MScA program, he accepted an entry-level job as a strategy analyst at a specialized financial services company on a very small team.
“I was happy to start at the bottom and learn as much as I could,” he says. “Working on such a small team was a great thing because I was able to learn a lot from my manager and take on projects that I had no experience with,” he says.
A demanding schedule will preclude most graduate students from working for a company unwilling to make accommodations, so the importance of finding an employer willing to work around it almost goes without saying. But this consideration needs to go both ways.
Rosenblum found his MScA program instructors to be respectful of the demands of his day job. “They understand that, especially as a part-time student, you joined this program to learn the skills you need to get a new job. If you need to take a week off from the program to do something for work, they're also professionals and they understand that.
“When I started as a strategy analyst, I had an hour and a half commute to class. On class days, I would leave work at 4:30 or so. My employers were happy with me, so they were willing to invest in my education—and I was happy to work later whenever I needed to. They’ve been accommodating, I’ve been accommodating, and I’ve always been able to get to class on time.”